Paper Monitor

Sharneet Singh Jagirdar, Pradeep Kumar Gupta
Research Methods for Computing and Informatics
Published: 2026-03-13
From feed: (TITLE(ai PRE/3 sustainability))

This study analyses five decades of research at the intersection of computing, informatics, and organisational studies to trace trends in qualitative (e.g., discourse analysis, ethnography) and quantitative (e.g., experimental design, statistical modelling) methods. Emphasizing methodological pluralism, it bridges positivist and constructivist paradigms using a pragmatism-triangulation-bibliometrics framework (co-citation, co-word analysis) to map the evolution of computational research. It identifies emerging domains, such as AI ethics, reinforcement learning, and experimental designs for new technologies. Findings highlight how interdisciplinary methods are shaped by innovations from machine learning to quantum computing. Recommendations emphasise sustainable technology, ethical AI, and hybrid methods that balance both academic rigour and real-world relevance.

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Published by Next Gen Supply Chains AI Automation and Sustainability in A Disrupted World We didn't find an OA link, try to find a OA version on Google Scholar
Next Gen Supply Chains AI Automation and Sustainability in A Disrupted World
Published: 2026-03-06
From feed: (TITLE(ai PRE/3 sustainability))

In an era defined by global disruptions, sustainability mandates and rapid technological advancement, Next-Gen Supply Chains provides an essential roadmap for navigating the complex transformation of global supply networks. This comprehensive book moves beyond isolated trends to present an integrated framework where generative AI, blockchain, autonomous systems and the Internet of Things converge to build resilient, efficient and responsible operations. This book masterfully bridges the critical gap between theoretical concepts and practical implementation, offering readers actionable strategies, detailed case studies from industry leaders such as Amazon and Lenovo, and robust frameworks for risk management, ethical AI governance and circular economy integration. With its unique emphasis on the synergy between technological innovation and the necessary human capital development, the book is an indispensable resource for supply chain executives, operations managers, technology implementers and academics seeking to future-proof their organizations and master the strategic imperatives of the modern supply chain landscape.

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Jorge Alejandro Silva
Sustainability Switzerland.
Published: 2026-02-23
From feed: (TITLE(ai PRE/3 sustainability))

Water systems experience increasing sustainability challenges from climate variability, aging infrastructure, and energy and chemical intensity demands, but AI has typically been assessed against prediction accuracy rather than demonstrated operational success. This PRISMA 2020 systematic review analyzed the role of AI solutions on sustainability in distribution, treatment, and basin management. The database search identified 920 records; after deduplication (n = 185), screening was conducted on n = 735 titles/abstracts and examination of the full text for n = 85, providing a total of n = 41 included peer-reviewed studies for qualitative synthesis and n = 38 for quantitative/bibliometric synthesis with the additional analysis of seven grey-literature sources. Evidence mapping reveals high growth post-2020, and distribution and wastewater operations are dominated by a few companies. The most deployable evidence is found with monitoring, anomaly/leak detection, and short-term forecasting, while optimization and reinforcement-learning control are primarily simulation validated with limited field applications. While accuracy metrics are often reported, transformation into water saved, kWh/m3, chemicals, compliance/reliability/resilience/equity measures are inconsistently and less frequently operationalized. In general, AI is most believable when it is part of analysis-ready workflows, bounded decision support, and measurement-and-verification.

Source | Open access

Muhammad Usman Tariq
Strategic Innovation for Sustainable Aviation Management
Published: 2026-02-18
From feed: (TITLE(ai PRE/3 sustainability))

The global aviation industry is under more pressure than ever to reduce carbon emissions because of stricter climate rules, higher fuel prices, and an increasing demand for eco-friendly travel options. AI provides innovative solutions that make operations more efficient and less harmful to the environment. This study looks at how AI tools, such as predicting when equipment needs fixing, planning the most efficient flight routes, and estimating passenger numbers, help airlines become more sustainable. It also examines how AI connects with environmental goals through the shift to digital technologies, showing how it helps reduce emissions and fits with international rules such as CORSIA and the EU ETS. The paper also looks at the difficulties in implementing AI, the systems in place to manage it, and the policies that help or hinder its use in both developed and growing markets. By seeing digital sustainability as a key part of their strategy, airlines can accelerate their path to becoming carbon neutral while also making their businesses stronger and more valuable in the long run.

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Victor James C. Escolano, Yann Mey Yee, Wei-Jung Shiang, Alexander A. Hernandez, Do Van Nang
Information Switzerland.
Published: 2026-02-15
From feed: (TITLE(ai PRE/3 sustainability))

Generative AI offers promising potential to promote environmental sustainability through personalized recommendations that influence individual behavior. This study examines the factors influencing the adoption and actual use of generative AI recommendations for environmental sustainability among Gen Z users in the Philippines by integrating the Theory of Planned Behavior (TPB) and the Technology–Environmental, Economic, and Social Sustainability Theory (T-EESST) with key generative AI attributes, together with trust and perceived risk. Survey data were collected from 531 Gen Z users in higher education institutions in the National Capital Region (NCR), Philippines, and analyzed using a hybrid SEM and ANN approach. Results from SEM indicate that key AI attributes, namely perceived anthropomorphism, perceived intelligence, and perceived animacy, significantly influenced users’ attitude towards generative AI recommendations. Attitude, perceived behavioral control, and trust emerged as significant predictors of behavioral intention, which have an eventual positive relation to actual use and environmental sustainability outcomes. In contrast, subjective norms and perceived risk did not significantly affect behavioral intention, which may suggest that Gen Z users’ engagement with generative AI for environmental sustainability is primarily driven by internal evaluations, perceived capability, and trust rather than social pressure or risk concerns. Complementing these findings, the ANN analysis identified perceived behavioral control, attitude, and trust as the most important factors, reinforcing the robustness of the SEM results. Overall, this study integrates existing sustainability and technology-adoption literature by demonstrating how generative AI recommendations can support environmental sustainability among Gen Z users by combining behavioral theory, sustainability theory, and AI attributes through a hybrid SEM–ANN approach in the context of a developing country.

Source | Open access

Amanda Balasooriya, Darshana Sedera
Business Strategy and the Environment
Published: 2026-01-26
From feed: (TITLE(ai PRE/3 sustainability))

ABSTRACT Although artificial intelligence (AI) is increasingly being touted to assist organizations, AI integration for sustainability efforts has been limited AND sporadic and tends to follow an ad hoc strategy. The existing literature therein focuses on the technological capabilities of AI, overlooking how organizations make sense of and strategically embed these into sustainability initiatives. Addressing this gap, this study explored the strategies that organizations utilized to integrate AI to improve sustainable initiatives in the Australian industry. Using a qualitative case study approach, data were collected through semistructured interviews with top executives and managers across operations, supply chain, and IT fields. Four strategies were discovered during the analysis, which was guided by grounded theory and organizational sensemaking theory as the sensitizing concept. The findings reveal that integrating AI for sustainability practices is a dynamic sensemaking process that reconfigures how organizations interpret value, responsibility, and interdependencies across the broader ecosystem.

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Chao Gong, Mengru Li, Jing Tian, Binghui Cai, Jiahang Wu, Bingyan Zhang, Xiaoyan Zhu, Shuai He, Pei Liu
Results in Chemistry.
Published: 2026-01-03
From feed: (TITLE(ai PRE/3 sustainability))

The green synthesis of Zinc Oxide Nanoparticles (ZnO NPs) has garnered significant attention due to its eco-friendly nature and the versatile applications of ZnO NPs in various fields. This review delves into the latest advancements in green synthesis methods, emphasizing plant extracts, microorganisms, and biomacromolecules (notably proteins and peptides). By integrating bibliometric analysis of Web of Science data (2015–2025), it pinpoints global research hotspots like “green synthesis,” “antibacterial activity,” and “photocatalysis.” Key insights include: plant-mediated synthesis offers cost-effectiveness but is affected by seasonal variability; microorganism-driven approaches allow for scalability yet require strict sterility; and biomacromolecules-mediated methods. Critically, we highlight how artificial intelligence (AI), particularly machine learning (ML), is being integrated across these methods—from predicting optimal plant extract compositions and microbial culture conditions to designing peptide templates—to enhance reproducibility, yield, and functionality of green-synthesized ZnO NPs. The bibliometric analysis reveals India and China as research hubs, with “Good Health and Well-Being” being the dominant sustainable development goal (4720 publications). The synthesis of sustainable chemistry and AI promises future breakthroughs in large-scale, tailored ZnO NPs production for applications in biomedicine and environmental protection. This review provides a comprehensive analysis integrating green synthesis techniques with scientometric insights, offering a critical assessment of ZnO NPs sustainability and its role in advancing global health solutions.

Source | Open access

Hendriadi Hendriadi, Megawaty Megawaty
Aip Conference Proceedings
Published: 2026-01-01
From feed: (TITLE(ai PRE/3 sustainability))

written by Hendriadi Hendriadi, Megawaty Megawaty Published by Aip Conference Proceedings

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Thavavel Vaiyapuri, Karthiyayini Murugesan
International Journal of Electrical and Electronic Engineering and Telecommunications.
Published: 2026-01-01
From feed: (TITLE(ai PRE/3 sustainability))

The integration of Terrestrial Networks (TNs) and Non-Terrestrial Networks (NTNs) is a foundational requirement for Sixth Generation (6G), enabling ubiquitous, resilient, and globally inclusive connectivity. However, existing surveys typically analyze this integration by concentrating on individual dimensions—such as architectural design, control and virtualization mechanisms, or Artificial Intelligence (AI)—while giving limited attention to sustainability considerations. This paper addresses this gap by introducing a unified architecture–AI–sustainability triadic framework, which forms the core contribution of the review. First, the paper provides a structured architectural synthesis that clarifies how different integration models influence the design and operational behavior of TN–NTN systems. Second, it consolidates the role of AI in enabling intelligent, adaptive, and context-aware network operation across integrated space–air–ground environments. Third, it advances sustainability as a primary design principle by synthesizing emerging strategies aimed at improving energy and carbon efficiency in future 6G infrastructures. By examining these three dimensions collectively, the review offers a coherent and comprehensive perspective on TN–NTN convergence, identifies persistent challenges including interoperability limitations and standardization gaps, and outlines future research directions needed to develop resilient, intelligent, and environmentally responsible 6G ecosystems aligned with United Nation Sustainable Development Goals (UN SDGs).

Source | Open access

Ben Kei Daniel, Nataliya Podgorodnichenko, Sarah Carr
Discover Computing.
Published: 2026-01-27
From feed: (TITLE(ai PRE/3 sustainability))

Artificial intelligence (AI) is receiving increasing attention for its potential to support sustainability in higher education. As both users and developers of AI technologies, universities are well-positioned to advance environmental and social goals while ensuring that AI is used responsibly. This scoping review examines two related areas: AI for Sustainability, which involves using AI to achieve sustainability outcomes, and sustainable AI, which focuses on reducing the direct environmental and ethical impacts of AI itself. The review examines how these concepts are defined in literature, the field's evolution over time, and the potential or increasing application of AI in sustainability practices by universities. While there are promising examples of the use of AI to advance sustainability projects, including AI used for energy management, climate monitoring, and green campus programs, many of these efforts are still limited in scale and lack clear ethical or environmental guidelines. In addition, much of the current research is either conceptual or based on small-scale pilot projects, with few studies assessing long-term impact or full institutional adoption and commitment. The outcome of the review is intended to provide a structured overview of the extant of work in the area; identify research gaps, and introduce the concept of a future green campus. It argues that future research should focus on testing AI tools in real university settings, measuring their environmental impact, and developing policies that combine ethical and sustainability goals. Greater involvement from students, staff, and faculty will also be essential to ensure that AI supports a fair and sustainable future for higher education.

PDF | Source | Open access

G. R. Manjunathareddy
AI and Sustainability for Global Security
Published: 2026-01-22
From feed: (TITLE(ai PRE/3 sustainability))

The accelerating pace of global environmental degradation—from climate change to biodiversity loss—demands bold and innovative solutions. This chapter explores how artificial intelligence (AI) is emerging as a transformative force in tackling these challenges. By harnessing technologies such as machine learning, computer vision, and natural language processing, AI offers unprecedented capabilities for monitoring ecosystems, forecasting climate impacts, managing resources efficiently, and guiding sustainable development. AI with complementary technologies like IoT, GIS, and cloud computing enhances data-driven decision-making across agriculture, urban planning, energy, water, and biodiversity conservation. However, the application of AI also raises ethical, social, and environmental concerns that must be addressed to ensure responsible and inclusive deployment. Through a multidisciplinary lens, this chapter examines AI's potential, limitations, and implications in advancing environmental sustainability, aiming to inform policy and practice for a resilient and equitable future.

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Satya Subrahmanyam
AI and Sustainability for Global Security
Published: 2026-01-22
From feed: (TITLE(ai PRE/3 sustainability))

The integration of artificial intelligence (AI) into global sustainability presents a transformative opportunity to address critical challenges such as climate change, resource depletion, and social inequities. This chapter explores AI's role in achieving the United Nations Sustainable Development Goals (SDGs) through innovative applications in environmental conservation, resource efficiency, and social impact. It examines AI-driven advancements in climate modeling, renewable energy optimization, and equitable economic development. Additionally, the chapter highlights challenges including ethical concerns, technological limitations, and policy gaps. Future trends, such as AI-IoT convergence and international collaborations, are discussed as key drivers of sustainable innovation. Through case studies and policy recommendations, this chapter provides a roadmap for leveraging AI to build a more sustainable and equitable world.

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Chedlia Farhat, Mouna Mouelhi
AI and Sustainability for Global Security
Published: 2026-01-22
From feed: (TITLE(ai PRE/3 sustainability))

This study examines the interplay between artificial intelligence, sustainable finance, and environmental sustainability in four OECD countries from 2007 to 2023. Using the panel ARDL method, our analysis shows that AI reduces carbon emissions, particularly in the long run. Initially, AI investments lead to a temporary rise in emissions due to implementation costs, but this trend reverses over time, aiding carbon neutrality and supporting SDGs, especially SDG 7 and SDG 13. The findings stress the need for short-term fiscal incentives and long-term regulatory frameworks integrating AI into sustainable finance. This research enriches the literature by addressing AI's overlooked role in sustainability and providing policy insights. Future studies could expand the geographic scope and examine AI's specific contributions to sustainable finance.

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Ayoub Louja, Yassin Zaiouane, Abdellah Jamali, Najib Naja
Industry 5 0 and Sustainable Development Theoretical Foundations and Practical Applications
Published: 2026-01-15
From feed: (TITLE(ai PRE/3 sustainability))

Although eye rubbing is known to contribute significantly to the progression of keratoconus and corneal ectasia following refractive surgery, no commonly used device objectively measures this behavior. A crucial gap still exists in the integration of real-time sustainability governance systems for healthcare applications with ethical artificial intelligence (AI) frameworks, despite notable advancements in AI and the Internet of Things (IoT). We addressed this by creating a smartwatch-based application that uses deep learning algorithms, more precisely a long short-term memory (LSTM) architecture, to analyze sensor data collected during simulated eye rubbing and normal daily activities performed by four trained participants representing both patient and healthy control behaviors. The result allows for proactive interventions that can improve patient outcomes in the treatment of keratoconus and post-refractive surgery care. This study highlights the potential of combining ethical AI and IoT solutions to improve sustainability governance in healthcare and confirms that automated detection of eye rubbing using cutting-edge deep learning techniques is feasible.

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Thao Tran Le Tuyet, Khoi Minh Nguyen
Cognition Technology and Work
Published: 2026-01-05
From feed: (TITLE(ai PRE/3 sustainability))

written by Thao Tran Le Tuyet, Khoi Minh Nguyen Published by Cognition Technology and Work

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Published by AI and Sustainability for Global Security We didn't find an OA link, try to find a OA version on Google Scholar
AI and Sustainability for Global Security
Published: 2026-01-22
From feed: (TITLE(ai PRE/3 sustainability))

written by Published by AI and Sustainability for Global Security

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Doha Ben Rouane, R. Mghaiouini, Ahmed Ait Errouhi
E3s Web of Conferences.
Published: 2026-01-01
From feed: (TITLE(ai PRE/3 sustainability))

The growing global need for freshwater has led to greater dependency on seawater desalination. This field has often been criticized for its high energy use and environmental concerns. Various desalination methods have been developed, including membrane, thermal and hybrid systems; however, their environmental impacts differ from situation to situation. This paper puts forward a framework for the first time utilizing Artificial Intelligence (AI) to analyze to evaluate and classify the various methods of desalination technologically using vast amounts of scientific literature. With the use of Natural Language Processing (NLP), machine learning, and automated data mining, the framework captures the main operational parameters, energy consumption, and environmental consequences within over twenty years of research. The data are then subjected to AI-aided multi-criteria decision-making to evaluate each technique and classify it by its environmental sustainability. The findings prove, i.e. on the highly heterogeneous and heavily biased environmental data that AI improves the precision, efficiency, and neutrality of environmental assessments. This research provides a template and a basis for extensive automated Artificial Intelligence; it also improves the efficiency of environmental assessment and optimization of desalination systems.

Source | Open access

Published by Revolutionizing the Cloud Generative AI Security and Sustainability. We think we have found an OA link here: this site
Revolutionizing the Cloud Generative AI Security and Sustainability.
Published: 2026-01-01
From feed: (TITLE(ai PRE/3 sustainability))

The cloud, once a revolutionary enabler of scale and agility, is itself undergoing a transformation, powered by the convergence of generative AI, security, and sustainability imperatives.We are witnessing a profound shift in how cloud systems are architected, operated, and governed.This book, Revolutionizing the Cloud: Generative AI, Security, and Sustainability, is the product of collaboration with thought leaders, researchers, and practitioners who are actively shaping this new era.Each chapter captures a distinct facet of the transformation, from zero-trust AI security models and cost-aware sustainability strategies to the ethics of AI and the evolving edge-cloud ecosystem.When we began curating this volume, we had one goal: to bring together perspectives that do not just explain where we are today, but boldly chart where we are headed next.The authors you will read here offer more than just technical depth; they offer vision.Many are leading major cloud modernization efforts, building AI-native platforms, or defining sustainability metrics that go beyond cost optimization to include carbon efficiency and social responsibility.This is not a book of hypotheticals.It is a book of emerging practice.Whether you are a cloud architect, AI researcher, cybersecurity leader, or policy maker, we hope that this collection informs your decisions, sparks new ideas, and equips you to build responsibly in an AI-powered future.We are grateful to all the contributors who brought their expertise and insight to this project.Their work represents not just where the industry is, but where it needs to go, toward systems that are intelligent, secure, and sustainable by design.

Source | Open access

Vivekanandan N.
Journal of Engineering Education Transformations
Published: 2026-01-01
From feed: (TITLE(ai PRE/3 sustainability))

written by Vivekanandan N. Published by Journal of Engineering Education Transformations

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Oshani Weerakoon, Shola Oyedeji, Md Abdus Samad, Abdulkadir Abubakar, Ahsan Ishan, Muhammad Shayan, Tuomas Mäkilä, Erkki Kaila
Lecture Notes in Business Information Processing.
Published: 2026-01-01
From feed: (TITLE(ai PRE/3 sustainability))

Generative AI presents growing opportunities across various fields, including Requirements Engineering (RE). RE, which is the backbone of software projects, drives the entire product development toward respective business goals. However, sustainability is not considered a primary need during the requirement elicitation, and as a result, software engineers are usually unable to envision the sustainability impacts of the products they build. To explore this gap, we introduce Reqwire, an AI-driven, sustainability-aware multi-agent system that (i) generates user stories from software requirement documents, (ii) enriches them with sustainability attributes, and (iii) integrates with common agile project management tools, like Jira. The system consists of specialized agents, namely as root, distributor, user story generator, and Jira. We followed a Design Science Research (DSR) approach under seven iterative cycles, incorporating feedback from an industry partner and academia to design and evaluate the Reqwire workflow. Our results indicate that Reqwire reduces manual effort by generating structured user stories, estimating story points, and assigning sustainability tags across five sustainability dimensions: environmental, economic, social, individual, and technical. The multi-agent-based framework enables integration with third-party tools, supporting consistent, systematic project tracking. Reqwire shows promise for enhancing agile workflows and promoting sustainable software practices from initial test rounds with the client.

Source | Open access

Rajib Bandyopadhyay
Artificial Intelligence AI for IT Energy Efficiency and Green AI for Environment Sustainability
Published: 2026-01-01
From feed: (TITLE(ai PRE/3 sustainability))

written by Rajib Bandyopadhyay Published by Artificial Intelligence AI for IT Energy Efficiency and Green AI for Environment Sustainability

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Abdussalam T. Mohamed, Hamed H. Aly, Timothy Little
Studies in Computational Intelligence
Published: 2026-01-01
From feed: (TITLE(ai PRE/3 sustainability))

written by Abdussalam T. Mohamed, Hamed H. Aly, Timothy Little Published by Studies in Computational Intelligence

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Franco Greco, Andrea Fronzetti Colladon, Peter A. Gloor
Contributions to Economics
Published: 2026-01-01
From feed: (TITLE(ai PRE/3 sustainability))

written by Franco Greco, Andrea Fronzetti Colladon, Peter A. Gloor Published by Contributions to Economics

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Kumar M.
Automating Intelligence with Cloud Native AI Tools
Published: 2026-01-01
From feed: (TITLE(ai PRE/3 sustainability))

written by Kumar M. Published by Automating Intelligence with Cloud Native AI Tools

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Published by Artificial Intelligence AI for IT Energy Efficiency and Green AI for Environment Sustainability We didn't find an OA link, try to find a OA version on Google Scholar
Artificial Intelligence AI for IT Energy Efficiency and Green AI for Environment Sustainability
Published: 2026-01-01
From feed: (TITLE(ai PRE/3 sustainability))

written by Published by Artificial Intelligence AI for IT Energy Efficiency and Green AI for Environment Sustainability

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Al-Khalili S.M.N.Z.
World Journal of Science Technology and Sustainable Development
Published: 2026-01-01
From feed: (TITLE(ai PRE/3 sustainability))

written by Al-Khalili S.M.N.Z. Published by World Journal of Science Technology and Sustainable Development

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Ngoc-Hong Duong, Hoang Ngoc Hung, Nguyen Hong An, Limin Bao, Tran Thao Nguyen, Dao Xuan Quang
Acta Psychologica.
Published: 2025-12-16
From feed: (TITLE(ai PRE/3 sustainability))

This study examines how employees' perceptions of AI trust and AI threat shape their collaboration with AI and subsequent career sustainability, grounded in Protection Motivation Theory and Person-Environment Fit Theory. Using data collected from 532 employees through an online survey, the relationships among constructs were analyzed using PLS-SEM. Results reveal that trust in AI exerts a positive and significant influence on employee-AI collaboration, while threat perception indirectly affects collaboration through motivational pathways. Furthermore, protean career orientation positively moderates the link between AI trust and collaboration, indicating that self-directed career attitudes strengthen adaptive engagement with AI systems. These findings underscore that approach and avoidance motivation play distinct roles in shaping collaboration outcomes, which in turn enhance various facets of career sustainability. The study contributes to theory by integrating cognitive appraisal and motivation perspectives to explain human-AI dynamics and provides managerial implications for fostering effective AI integration and sustainable career development in the digital workplace.

Source | Open access

Elaine Tan
Handbook of Artificial Intelligence in Higher Education
Published: 2025-12-16
From feed: (TITLE(ai PRE/3 sustainability))

written by Elaine Tan Published by Handbook of Artificial Intelligence in Higher Education

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Przemek Pospieszny, Dominika P. Brodowicz
Sustainable Development.
Published: 2025-12-14
From feed: (TITLE(ai PRE/3 sustainability))

ABSTRACT In the past few years, the evolution of artificial intelligence (AI), particularly generative AI (GenAI) and large language models (LLMs), has made human‐computer interactions more frequent, easier, and faster than ever before. This brings numerous benefits in terms of enhancing efficiency, accessibility, and convenience in various sectors from banking to health. AI tools and solutions applied in computers and communication devices support decision‐making processes and managing operations of users on the individual level as well as organisationally, including resource allocation, workflow automation, and real‐time data analysis. However, the current use of AI carries a substantial environmental footprint due to its reliance on high‐computational cloud resources. In such a context, this paper introduces the concept of agentic environments, a sustainability‐oriented AI framework that goes beyond reactive systems by leveraging GenAI, multi‐agent systems, and edge computing to minimize the negative impact of technology. These types of environments can contribute to the optimization of resource use, enhanced quality of life, and prioritization of sustainability while at the same time safeguarding user privacy through decentralized, edge‐driven AI solutions. Based on both secondary and primary data gathered during a focus group and semi‐structured interviews with AI professionals from leading technology companies, the authors provide a conceptual framework of agentic environments and discuss it in the context of three lenses, including personal sphere, professional and commercial use, and urban operations. The findings include the potential of agentic environments to foster sustainable ecosystems, mainly due to the optimisation of resource usage and securing the privacy of data. The study outlines recommendations for implementing edge‐driven deployment models to reduce dependency on currently widely applied high‐energy cloud solutions.

Source | Open access

Kunal Gagneja, Sapna Singh, Zainab. R. Abdulsada, Shivani Pant, Rami Riad Hussien
Artificial Intelligence Technologies for Smart and Sustainable Urban Transportation Integrated Platforms and Use Cases
Published: 2025-12-05
From feed: (TITLE(ai PRE/3 sustainability))

Throughout the chapter, the need for responsible and green implementation of artificial intelligence (AI) is discussed, emphasising the need for AI practices that are both ethical and sustainable. The complex nature of AI models has resulted in increased energy, water, and resource requirements, creating a concern for the environment. E-waste generation, energy consumption, and carbon footprint are some of the key environmental impacts. Throughout the chapter, strategies including optimising algorithms, using renewable energy, and promoting efficient computing infrastructure are highlighted as means of ensuring AI's sustainability both as an input and as an output. This paper discusses how AI can be used to optimise energy consumption, predict climate, improve mobility, and conserve biodiversity. The chapter emphasises the importance of balancing technological advances with ecological responsibilities when it comes to sustainable AI.

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Adel Ben Youssef, Nessrine Omrani, Adelina Zeqiri
IEEE Transactions on Engineering Management
Published: 2025-12-04
From feed: (TITLE(ai PRE/3 sustainability))

This paper examines the potential of artificial intelligence (AI) to facilitate sustainable development within smart manufacturing globally, addressing a significant literature gap. While AI clearly plays an important role in operational efficiency, its broader contribution across the manufacturing life cycle remains underexplored. Drawing on 43 expert interviews conducted between June and September 2024, four areas where AI delivers sustainability value are identified: energy optimization, predictive maintenance, sustainable supply chains, and carbon emission management. The robustness of the results is ensured by the Gioia protocol, double coding with adjudication, cross-role and cross-country triangulation, member checking, saturation, and an auditable codebook. First, the findings reveal that many small and medium-sized enterprises (SMEs) experience greater than expected efficiency gains when moving from manual to AI-based energy monitoring, exposing hidden inefficiencies. Second, a strong link between predictive maintenance and energy savings is revealed, suggesting that equipment longevity and sustainability are more tightly coupled than previously recognized. Third, AI-enabled emission tracking serves both as a compliance mechanism and as a catalyst for internal cultural change and stakeholder trust, positioning AI as a strategic governance tool. Finally, policy and managerial recommendations are provided to foster responsible AI adoption in manufacturing. A multilevel conceptual model that illustrates how AI, supported by digital and organizational enablers, contributes to sustainability outcomes and alignment with the Sustainable Development Goals (SDGs) is also presented. This research advances theory and practice by highlighting the transformative, yet still underleveraged, role of AI in sustainable manufacturing transitions.

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Justyna Berniak‐Woźny
Economics and Environment.
Published: 2025-12-03
From feed: (TITLE(ai PRE/3 sustainability))

Purpose – This study explores the evolving role of artificial intelligence (AI) in sustainability and ESG reporting, mapping research trends, contributions, and future directions. Methodology/Approach – A two-stage methodology combining bibliometric analysis (304 SCOPUS-indexed documents from 2015–2025) with qualitative content analysis of 18 key publications was applied. Findings—AI enhances ESG reporting through automation, real-time monitoring, predictive analytics, and improved data integrity. These capabilities support organisations in aligning with international standards and responding to growing stakeholder demands for transparency and accountability. Research Implications – While conceptual frameworks are emerging, empirical validations and sectoral comparisons remain limited. Practical Implications – AI supports more efficient, transparent, and standards-aligned reporting processes, enabling better decision-making and risk mitigation. Social Implications – Ethical and inclusive design of AI systems is crucial to prevent bias and enhance stakeholder trust. Originality/Value – This study offers a comprehensive bibliometric perspective, identifies key AI-enabled advancements, and proposes future research avenues.

Source | Open access

Published by Integrating AI and Sustainability in Technical and Vocational Education and Training Tvet We didn't find an OA link, try to find a OA version on Google Scholar
Integrating AI and Sustainability in Technical and Vocational Education and Training Tvet
Published: 2025-04-24
From feed: (TITLE(ai PRE/3 sustainability))

written by Published by Integrating AI and Sustainability in Technical and Vocational Education and Training Tvet

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YongGang Weng, Farah Akmar Anor Salim, Hanini Ilyana Che Hashim, Budi Setiawan, Mohamed Faiz Ramli
Malaysian Journal of Consumer and Family Economics.
Published: 2025-12-01
From feed: (TITLE(ai PRE/3 sustainability))

Artificial intelligence (AI) has emerged as a transformative driver for entrepreneurial development, particularly among women entrepreneurs seeking inclusive and socially sustainable business outcomes. This study conducts a systematic literature review (SLR) guided by the PRISMA framework to synthesise research published between 2022 and 2024 on the integration of AI in supporting women-led enterprises. Searches were conducted using Scopus and Web of Science, resulting in 17 high-quality journal articles that met the inclusion criteria. The analysis identifies three overarching themes: (1) AI as an enabler of social and economic empowerment, (2) AI-driven decision-making and capability development, and (3) organisational and leadership diversity in AI-enabled ventures. Findings reveal that AI contributes to improved business resilience, enhanced decision-making, and expanded market participation for women entrepreneurs. However, gaps remain in empirical testing, theoretical development, and contextual representation from developing economies. The review highlights key implications for policymakers, technology developers, and entrepreneurship support ecosystems, emphasising the need for gender- inclusive AI policies and capacity-building initiatives. This study contributes to the growing body of knowledge by integrating AI, gender, and social sustainability perspectives within a unified analytical framework.

Source | Open access

Jesús Heriberto Orduño-Osuna, Abelardo Mercado-Herrera, Juan Carlos Ling-Lopez
Building A Sustainable Future at the Intersection of Semiconductors Energy and AI
Published: 2025-11-28
From feed: (TITLE(ai PRE/3 sustainability))

This chapter examines the convergence of global decarbonization and the exponential growth of computation, identifying the “Silicon Ceiling” as a fundamental bottleneck to modern progress. To address this, the work proposes a three-layered innovation framework: the Physical Layer, transitioning from silicon to Wide Bandgap semiconductors like Silicon Carbide and Gallium Nitride; the Cognitive Layer, where Artificial Intelligence acts as an active orchestrator of energy efficiency; and the Systemic Layer, which integrates these technologies into the metabolic ecosystem of the Smart City. By analyzing properties such as superior thermal conductivity and higher breakdown voltages, the text demonstrates how WBG materials enable the electrification of transport and hyperscale data centers. Furthermore, it explores how AI-driven predictive control and digital twins transform passive infrastructure into adaptive, resilient systems. The chapter concludes that sustainability in the digital era depends on the co-evolution of energy, cognition, and urban infrastructure.

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Komal Parashar
Journal of Internet Services and Information Security.
Published: 2025-11-26
From feed: (TITLE(ai PRE/3 sustainability))

The increasing use of multi-cloud infrastructures has created new difficulties in tracking and diagnosing performance issues across different cloud providers. Congestion in inter-domain traffic is a common issue, but traditional traceroute tools fall short of diagnosing inter-domain congestion for lack of perspective and bounded protocols. This paper introduces a diagnostic framework based on enhanced traceroute variants aimed at accurate bottleneck detection and localization in multi-cloud interconnects. The methodology applies a combination of active probing, delay correlation analysis, and next-hop verification to target specific delay hotspots and asymmetric routing patterns. Adaptive cloud network policies and configurations are addressed by a combination of Paris traceroute, TCP-based probing, and hybrid ICMP/TCP diagnostics. With the goal of multi-regional validation, experiments were conducted across AWS, Azure, and Google Cloud Platform from distributed vantage points. These findings suggest that cloud exchange points and ISP-level handoff zones often host traffic and congestion-based anomalies during peak traffic. Augmented asymmetry of routing and round-trip latency differences enrich bottleneck detection. The framework not only increases transparency for opaque multi-cloud architectures, but also enables cloud providers to make better informed decisions on interconnection and routing strategies. The specific method suggested here surpasses standard traceroute techniques in accuracy, detail, and level of confidence in the diagnosis, thereby enabling efficient real-time troubleshooting of cloud networks. The research highlights the need of specialized diagnostics in multi-cloud architectures while paving the way for further developments focusing on machine-learning-enabled automated root cause analysis.

Source | Open access

Marcellus Forh Mbah, Tsamarah Rana Nugraha, Iryna Kushnir
Sustainability Switzerland.
Published: 2025-11-26
From feed: (TITLE(ai PRE/3 sustainability))

The integration of generative artificial intelligence (Gen-AI) into sustainability education is justified by its potential to introduce sustainability perspectives through transformative learning. By encouraging individuals to critically reflect and challenge their prior beliefs and assumptions, Gen-AI can deepen their understanding of sustainability concepts and inspire long-term commitment to sustainable practices. While the broader educational potential of Gen-AI has been widely explored, previous research tends to overlook its specific benefits and implications within the context of sustainability education. This paper addresses this gap by exploring both the opportunities and challenges of employing Gen-AI in the context of sustainability education through a critical review of diverse outputs. A thematic analysis of the outputs reveals a complex interplay between the opportunities and challenges. While Gen-AI offers access to information, personalised learning, fosters creativity, and decision-making support, the associated challenges, such as unequal access, overreliance on use, unreliable outputs, and environmental cost, may undermine the opportunities and the broader efforts to foster sustainability. The originality of this paper lies in providing critical insights for institutions, educators, and policymakers seeking to harness generative AI to advance sustainability education, an area pivotal to the pursuit of a just and sustainable future.

Source | Open access

Goh Ying Yingsoon, Suyan Zhang, Nurul Ain Chua, Y. Chen, Tie Xiaoyao
Rethinking the Pedagogy of Sustainable Development in the AI Era
Published: 2025-03-14
From feed: (TITLE(ai PRE/3 sustainability))

This chapter explores the integration of cultural dimensions in AI-enhanced sustainability education, emphasizing the need to tailor pedagogies for a diverse global learner community. As sustainability challenges transcend geographical boundaries, it is imperative to develop educational approaches that are inclusive and culturally sensitive. The chapter discusses the potential of Artificial Intelligence (AI) in personalizing learning experiences and addressing the unique needs of learners from various cultural backgrounds. By examining case studies and empirical evidence, we demonstrate how AI can be leveraged to adapt sustainability education to different cultural contexts, fostering a deeper understanding and engagement among students. The chapter also highlights the importance of interdisciplinary collaboration and the role of educators in bridging cultural gaps, ultimately contributing to the development of a more sustainable and equitable global society.

Source

Kaelyn Ramsey, Sevinj Iskandarova
2025 IEEE Global Conference on Artificial Intelligence and Internet of Things Gcaiot 2025
Published: 2025-11-23
From feed: (TITLE(ai PRE/3 sustainability))

The integration of artificial intelligence (AI) into sustainable urban practices has emerged as a crucial catalyst for advancing smart city initiatives. This research explores the interplay between consumer attitudes and the adoption of AI technologies for sustainability within urban environments. Through a quantitative study involving 234 participants across the Eastern United States, we examine the impact of familiarity with AI, awareness of sustainability applications, community involvement, and perceived personal influence on attitudes toward AI-driven solutions. Our findings reveal that higher familiarity with AI correlates positively with trust and willingness to engage with sustainable practices, while skepticism remains prevalent among those less familiar. Despite the promising potential of AI in enhancing sustainability, barriers such as resistance to change, organizational constraints, and a lack of consumer confidence persist. To address these challenges, we recommend comprehensive educational campaigns, community engagement initiatives, ethical leadership, and streamlined regulatory frameworks. By fostering a culture of trust and understanding, stakeholders can effectively integrate AI technologies into smart city frameworks, enhancing urban sustainability and improving the quality of life for residents. This research highlights the imperative of aligning technological advancements with consumer needs and values to achieve ambitious sustainability goals.

Source

Andrea Cibrario, Sevinj Iskandarova
2025 IEEE Global Conference on Artificial Intelligence and Internet of Things Gcaiot 2025
Published: 2025-11-23
From feed: (TITLE(ai PRE/3 sustainability))

This study evaluates Italy’s AI strategy for promoting sustainable development within a broader European context. While Italy’s initiatives show promise, their success relies on sustained political commitment, effective data governance, and collaboration between public and private sectors. The role of consumer trust in AI and public awareness is also examined, revealing a largely optimistic outlook among Italian citizens regarding the potential benefits of AI, particularly in healthcare and environmental sustainability. Yet, significant gaps in knowledge and awareness persist, necessitating targeted educational initiatives and transparent communication to foster informed perspectives. In analyzing 184 adults across Milan and Turin, consumer perceptions towards AI-enabled sustainability solutions revealed a generally positive yet varied impact on sustainable behaviors, as well as mixed assessments of government policies related to AI and sustainability. The study findings suggest a call for collaborative efforts among regulators, researchers, and industry leaders to prioritize sustainable practices in AI development, emphasizing the critical balance between AI’s ecological impacts and its potential to drive sustainable progress.

Source

Tolga Kara
Sustainability Switzerland.
Published: 2025-11-16
From feed: (TITLE(ai PRE/3 sustainability))

This paper reviews the impact of various types of AI education on the sustainability attitudes of Generation Z in Istanbul in the context of the new model of Hospitality 5.0. The study concentrates on three major aspects of learning related to AI, namely knowledge, practical implementation, and value-based orientation, and their interim impact on shaping sustainability-oriented perceptions of young people. The study established the reliability and stability of the constructs using psychometric testing, and factor analysis and structural modeling proved that every educational dimension of AI is positively related to sustainability attitudes. Of them, knowledge utilization proved to be the most powerful predictor. Further residual results revealed behavioral anomalies by demonstrating that those who outperformed were people with moderate technical abilities and high sustainability values and those who underperformed possessed high digital abilities without integrity value-based alignment. These results demonstrate that formal AI education is a stabilizing force, which encourages more consistent and sustainability-focused attitudes. Altogether, the findings point to the significance of educational models combining technical skills with ethical and environmental consciousness in helping Generation Z to find a middle ground in terms of sustainable change in the digital hospitality industry.

Source | Open access

Márcia R. C. Santos, Luísa Cagica Carvalho, Edgar Francisco
Information Switzerland.
Published: 2025-11-14
From feed: (TITLE(ai PRE/3 sustainability))

As artificial intelligence (AI) technologies increasingly shape sustainability agendas, organizations face the strategic challenge of aligning AI-driven innovation with long-term environmental and social goals. While academic interest in this intersection is growing, research remains fragmented and often lacks actionable insights into the organizational capabilities needed to operationalize sustainable AI innovation. This study addresses this gap by exploring how knowledge-based organizational capabilities—such as absorptive capacity, knowledge integration, organizational learning, and strategic leadership—support the alignment of AI initiatives with sustainability strategies. Grounded in the knowledge-based view of the firm, we conduct a bibliometric and thematic analysis of 216 peer-reviewed articles to identify emerging conceptual domains at the nexus of AI, innovation, and sustainability. The analysis reveals five dominant capability clusters: (1) data governance and decision intelligence; (2) policy-driven innovation and green transitions; (3) digital transformation through education and innovation; (4) collaborative adoption for sustainable outcomes; and (5) AI for smart cities and climate action. These clusters illuminate the multi-dimensional roles that knowledge management and organizational capabilities play in enabling responsible, impactful, and context-sensitive AI adoption. In addition to mapping the intellectual structure of the field, the study proposes a set of strategic and policy-oriented recommendations for applying these capabilities in practice. The findings offer both theoretical contributions and practical guidance for firms, policymakers, and educators seeking to embed sustainability into AI-driven transformation. This work advances the discourse on innovation and knowledge management by providing a structured, capability-based perspective for designing and implementing sustainable AI strategies.

Source | Open access

Soumya Sankar Ghosh
Rethinking the Pedagogy of Sustainable Development in the AI Era
Published: 2025-03-14
From feed: (TITLE(ai PRE/3 sustainability))

This study explores how predictive analytics and Artificial Intelligence (AI) can revolutionize sustainability education by fostering personalized learning, ethical reasoning, and inclusivity. Statistical modeling demonstrated significant improvements in sustainability literacy across diverse cohorts, with the greatest gains observed among underperforming students. However, challenges such as algorithmic transparency, bias, and access disparities highlight the need for ethical AI design and infrastructural investments. Combining personalized AI-driven tools with human oversight emerged as essential for meaningful engagement and equity. Recommendations include developing ethical AI systems, fostering digital inclusion, and promoting interdisciplinary research to scale AI-driven sustainability education effectively.

Source

Zora Mária Frešová
Economics and Environment.
Published: 2025-11-13
From feed: (TITLE(ai PRE/3 sustainability))

This research explores the intersection of artificial intelligence (AI) and sustainability discourse, primarily focusing on public opinion expressed on the Reddit platform. Using unsupervised machine learning and large language models (LLMs), we conduct opinion mining and sentiment analysis on a diverse range of Reddit discussions related to sustainability, employing both fine-grained analysis and traditional statistical methods like bigram and frequency analysis. Our findings reveal key trends in public perception and evolving attitudes towards sustainability, highlighting areas of concern and potential opportunities for intervention. Additionally, we demonstrate how AI can significantly expedite model development, enabling rapid responses to shifts in public opinion. This agility is crucial for aligning sustainability initiatives with the values and concerns of diverse stakeholders. While acknowledging the limitations of Reddit as a representative sample of global opinion and the need for further validation of AI's capabilities in specific sustainability contexts, this study provides valuable insights into the dynamic relationship between AI and sustainability discourse. By understanding public sentiment and leveraging AI's potential for rapid adaptation and analysis, we can inform more effective strategies for addressing environmental challenges and promoting a sustainable future.

Source | Open access

Usharani Bhimavarapu
Green Software Engineering for Business Project Management
Published: 2025-11-10
From feed: (TITLE(ai PRE/3 sustainability))

The broad scope of applications of artificial intelligence (AI) in many fields has also raised important issues concerning responsible practice and sustainability of the environment. In the current research, designing ethical AI systems and their impact on the environment are argued with a deliberate effort at embracing responsible and sustainable technology. Information were collected from the site under construction via a preprocessed systematic investigation for validity and processed later using Particle Swarm Optimization (PSO) with feature selection in mind. Bi-stacked Gated Recurrent Unit (GRU) has been utilized to feature extract of temporal patterns within ethics and environmental features to facilitate predictive analysis and identify possible biases. The conclusion highlights the need to reconcile fairness, transparency, and accountability of AI systems with their carbon footprint.

Source

Wasswa Shafik
Reshaping Financial Systems and Fostering Sustainability Through Embedded Finance
Published: 2025-11-07
From feed: (TITLE(ai PRE/3 sustainability))

Artificial Intelligence (AI)-integrated embedded finance is transforming financial services by enabling seamless, data-driven solutions that promote sustainability. This study details how embedding AI-driven financial tools into non-financial platforms, businesses and consumers gain real-time access to personalized, transparent, and efficient financial services. AI enhances risk assessment, fraud detection, and credit scoring while optimizing resource allocation for sustainable investments. Its predictive analytics support green financing, ethical lending, and financial inclusion, fostering long-term economic and environmental benefits. Therefore, as AI continues to evolve, its integration in embedded finance holds the potential to drive a more resilient, inclusive, and sustainable financial ecosystem.

Source

Fátima País, Paulo Matos, Luís Henrique de Barros Soares, Joaquim Santos, Luís Conceição, Constantino Martins, Goreti Marreiros
Lecture Notes in Networks and Systems
Published: 2025-11-07
From feed: (TITLE(ai PRE/3 sustainability))

written by Fátima País, Paulo Matos, Luís Henrique de Barros Soares, Joaquim Santos, Luís Conceição, Constantino Martins, Goreti Marreiros Published by Lecture Notes in Networks and Systems

Source

Guangpeng Chen, Anthony S. David
Sustainability Switzerland.
Published: 2025-11-06
From feed: (TITLE(ai PRE/3 sustainability))

Artificial Intelligence (AI) is increasingly central to sustainable development, yet its advancement varies across G7 economies. This study employs Method of Moments Quantile Regression (MMQR) to examine how Financial Technology (FinTech), Economic Growth (EG), Human Capital (HC), and Renewable Energy Consumption (RENC) influence AI development in G7 countries from 2000 to 2022. By analyzing heterogeneous effects across quantiles, the study captures stage-specific drivers often overlooked in average-based models. Results indicate that FinTech and human capital significantly promote AI adoption in lower and middle quantiles, enhancing digital inclusion and innovation capacity, while RENC becomes relevant primarily at advanced stages of AI adoption. Economic growth exhibits negative or inconsistent effects, suggesting that GDP expansion alone is insufficient for technological transformation without alignment to supportive policies and institutional contexts. The lack of long-run cointegration further highlights the dominance of short- and medium-term dynamics in shaping the AI–sustainability nexus. These findings provide actionable insights for policymakers, emphasizing targeted FinTech development, skill-building initiatives, and renewable-powered AI solutions to foster sustainable and inclusive AI adoption. Overall, the study demonstrates how financial, human, and environmental factors jointly drive AI development, offering a mechanism-based perspective on technology-driven sustainable development in advanced economies.

Source | Open access

Amina Hamdouni
International Journal of Financial Studies.
Published: 2025-10-31
From feed: (TITLE(ai PRE/3 sustainability))

The objective of this study is to investigate how responsible AI governance mechanisms influence value creation and sustainability in Saudi banks over the period 2015–2024. Using a panel dataset from listed Saudi banks and combining ESG disclosure metrics with financial indicators, we investigate whether AI adoption and AI-related disclosures enhance banks’ market and accounting performance while strengthening sustainability outcomes. We apply robust panel regressions, control for bank-specific characteristics, and run sensitivity checks to address endogeneity and measurement concerns. The empirical findings indicate that higher levels of AI adoption are positively and significantly associated with both value creation and sustainability performance. Furthermore, Dumitrescu–Hurlin panel Granger causality tests confirm a unidirectional causal relationship from AI adoption to both financial and sustainability outcomes. Overall, the results suggest that responsible AI integration may enhance sustainable value creation in the Saudi banking sector.

Source | Open access

J. Niruba Rani, Sree Mathi S, Sumyutha L, Surya V, Swetha Krishna T S, Thangavel R
2025 IEEE International Conference on Blockchain and Distributed Systems Security Icbds 2025
Published: 2025-10-30
From feed: (TITLE(ai PRE/3 sustainability))

Sustainable practices are increasingly significant in the contemporary drug industry to maintain customers' trust and promote green products usage. This article presents an innovative strategy for sustainability marketing that is driven by artificial intelligence and tailored to the pharmaceutical industry. It employs analytics of customer behavior and advanced machine learning algorithms to enhance the visibility and credibility of green products. Ensuring green activities are conveyed appropriately and focused, the proposed strategy leverages AI in analyzing consumer trends, predicting sustainability mindsets, and real-time adjustment of marketing campaigns. The approach assists pharmaceutical companies in taking preventive measures to highlight eco-friendly measures by determining key factors affecting customer trust and buying behavior through the utilization of sentiment analysis and natural language processing. The AI-based approach beats traditional methods in customer engagement, brand image, and adoption rate of eco-friendly products, based on experimental testing on both actual and virtual data sets. The study indicates that drug firms that are concerned about the environment and wish to grow their business can potentially gain significantly from intelligent, data-savvy sustainability marketing.

Source

Turkay Ersener, Paris A. Fokaides
Lecture Notes in Civil Engineering.
Published: 2025-10-27
From feed: (TITLE(ai PRE/3 sustainability))

Abstract This paper presents an overview of how Artificial Intelligence (AI) supports the sustainability assessment of buildings, structured around the main phases of the building lifecycle. The review focuses on four core stages: design and planning, operation and monitoring, assessment and optimization, and compliance and certification support. Within this structure, the study highlights the growing role of AI in enhancing decision-making, improving building performance, and supporting sustainable outcomes across each phase. AI tools are categorized into four main groups: general-purpose platforms, data analytics environments, building-specific tools, and specialized applications. These are then mapped to key application domains such as prediction, simulation, decision support, and system optimization. The study emphasizes the connection between AI functionalities and specific needs in building sustainability, offering a structured approach for understanding the current landscape of tools and methods. By combining a lifecycle-based perspective with a classification of AI technologies and use cases, the paper aims to support researchers and practitioners in navigating the evolving intersection of AI and sustainable built environments. The findings serve as a foundation for further research and tool development, fostering more effective integration of AI in future sustainability assessments.

Source | Open access

Yu‐Ming Fei, J-P Liou, Peng Sun
Cleaner Engineering and Technology.
Published: 2025-10-26
From feed: (TITLE(ai PRE/3 sustainability))

This study investigates the dual impact of Artificial Intelligence (AI) and Robotic Process Automation (RPA) on sustainability performance and brand equity in Taiwan’s service sector. Grounded in the Resource-Based View (RBV) and the Technology–Organization–Environment (TOE) framework, the research adopts a mixed-methods case study design involving three award-winning small-to-medium enterprises (SMEs) that participated in a national AI transformation program. By integrating digital tools such as AI-generated content platforms, RPA-enabled marketing automation, and intelligent energy management systems, these enterprises aimed to reduce material consumption, enhance customer engagement, and strengthen brand identity. Empirical evidence drawn from enterprise resource planning (ERP) records, carbon audits based on the GHG Protocol, and a structured survey of 121 employees demonstrates notable outcomes: reduced electricity usage and printing costs, increased digital marketing efficacy, and improved perceptions of organizational innovation and sustainability. Statistical analyses reveal significant between-firm differences in perceived benefits, while employee attitudes toward AI and RPA were consistently positive across dimensions. These findings highlight how AI and RPA can serve as catalysts for cleaner operational practices and strategic brand development, aligning with the journal’s focus on engineering solutions for sustainable consumption and technological advancement. • Demonstrates how AI and RPA adoption jointly enhance environmental sustainability and brand equity in service-sector SMEs. • Develops an integrated analytical framework combining the Technology–Organization–Environment (TOE) and Resource-Based View (RBV) perspectives. • Provides empirical insights from a multi-case study of three award-winning Taiwanese service enterprises. • Shows that intelligent automation supports green innovation through reduced energy use and paper consumption while strengthening customer engagement. • Offers actionable recommendations for sustainable digital transformation policies in service-based industries.

Source | Open access

Maria da Conceição Tavares, Daniel G. Gomes, Jorge Chicoca, Maria Rodrigues Pinto, Romildo Silva
Lecture Notes in Networks and Systems
Published: 2025-10-17
From feed: (TITLE(ai PRE/3 sustainability))

written by Maria da Conceição Tavares, Daniel G. Gomes, Jorge Chicoca, Maria Rodrigues Pinto, Romildo Silva Published by Lecture Notes in Networks and Systems

Source

Dhiya Al‐Jumeily, Jamila Mustafina, Manoj Jayabalan
Sustainability Switzerland.
Published: 2025-10-15
From feed: (TITLE(ai PRE/3 sustainability))

Dear colleagues, researchers, practitioners, professionals, students, and all those sincerely committed to exploring pathways toward a harmonious future, to all who observe with both enthusiasm and thoughtful reflection the ways in which artificial intelligence increasingly permeates the fabric of our daily lives and shapes the trajectory of societal development [...]

Source | Open access

Himanshu Gupta, Manu Vallabh Mishra Mishra, Nitin Grover, Ravi Chourasia
AI Enabled Sustainable Innovations in Education and Business
Published: 2025-04-24
From feed: (TITLE(ai PRE/3 sustainability))

The integration of project management with supply chain and big data engineering using AI methodologies has emerged as a strategic approach to enhance operational efficiency, decision-making, and sustainability. This chapter explores the intersection of these domains, highlighting how AI-driven insights and automation can streamline supply chain processes, improve data accuracy, and optimize resource allocation. AI methodologies such as machine learning, predictive analytics, and natural language processing enable real-time monitoring, demand forecasting, and risk mitigation, leading to improved supply chain resilience and reduced environmental impact. By embedding AI into supply chain management and project execution, organizations can achieve greater agility, cost savings, and sustainability, ensuring a competitive edge in a rapidly evolving market landscape. The chapter also discusses challenges and future opportunities in integrating AI-driven project management and supply chain engineering for long-term sustainable growth.

Source

Vikas Garg, Sailaja Bohara, Arpita Srivastava
Discover Sustainability.
Published: 2025-09-30
From feed: (TITLE(ai PRE/3 sustainability))

Artificial intelligence [AI] is transforming sustainability marketing by influencing consumer behaviour. This is because of the in terms of engagement with environmentally friendly brands due to increasing raising environmental consciousness and technological development. This paper will examine, through sustainable marketing practices, the impact that AI-enabled sustainability is having in changing the perceptions of consumers and creating a better relationship with sustainable companies. This paper examines sustainable marketing practices and the impact of AI-enabled sustainability on changing consumer perceptions, thereby creating a loyal relationship with brands that focus on sustainability. By employing a Systematic Literature Review [SLR] of 28 articles from the Scopus database, The study using a Systematic Literature Review [SLR] of 29 articles searched in scopus database, the article identifies The study employed the Antecedents-Decisions-Outcomes [ADO] framework, and to identify reveals the key themes, trends, and gaps in AI applications to sustainable marketing and consumer behaviour. The AI sphere is progressing towards the green marketing environment and consumer behaviour. The results demonstrate that consumer trust and loyalty to green brands are built through advanced AI technologies, including machine learning, natural language processing, and personalization. Environmental awareness is a factor that guides consumers to purchase sustainable products and healthy lifestyles, especially among younger generation customers, and the concept of. Green branding and sustainable packaging strengthen the brand’s credibility. The ADO framework emphasises how antecedents (eg, Technological and consumer factors) guide strategic decisions resulting in increased sales of green products, consumer retention of long-term consumers, and improved brand image. The thematic mapping done in this paper reveals motor themes key themes like green marketing, and AI, as well as emerging areas like consumer behaviour and social media, for future research. To make strategic decisions, such as increasing sales of green products, consumer retention of long-term consumers, and better brand image can be achieved. Motor themes [e.g., green marketing, AI] and new areas [e.g., consumption behavior, social media] that should be investigated in the future are revealed with the help of thematic mapping. This paper highlights the AI transformative role and innovative power of AI in aligning marketing strategies with sustainability. It guides researchers and practitioners who want to navigate this evolving landscape.

Source | Open access

Rakesh Koteshwar Ramesh
Addressing Sustainable Development Goals Through Competency Based Education
Published: 2025-09-26
From feed: (TITLE(ai PRE/3 sustainability))

This chapter explores Green Artificial Intelligence (Green AI) in education, focusing on embedding sustainability principles into algorithmic learning systems (ALSs). As AI becomes integral to education, concerns arise over its ecological footprint, from data storage to deployment. By integrating sustainable design into AI applications, the chapter highlights how Green AI aligns with the United Nations' SDGs, particularly SDG 4 (Quality Education) and SDG 13 (Climate Action). It covers the environmental challenges of traditional AI, the foundations of Green AI, and its potential in education through energy-efficient models, sustainable practices, and AI tools for environmental education. Case studies, such as AI-assisted recycling and energy-efficient platforms, offer practical insights. The chapter also discusses AI ethics, policy implications, and metrics for assessing Green AI's long-term impact, with recommendations for responsible, equitable, and sustainable educational technologies.

Source

Joanna Rosak-Szyrocka, Radosław Wolniak
AI Driven Sustainability the Future of Human Resources Management
Published: 2025-09-25
From feed: (TITLE(ai PRE/3 sustainability))

AI-Driven Sustainability: The Future of Human Resources Management is an interdisciplinary publication that demonstrates the artificial intelligence phenomen or AI in the sustainable shaping of human resources management. The role of AI as a support to the existing HR processes and a comprehensive changer of organizational, ethical, social, and environmental values is reviewed by authors. By providing readers with the wide scale of explanatory case studies among global business corporations and with a thorough review of law-ethical and technological issues in 10 chapters, the book suggests a “green model” of AI implementation in HR dimensions. A must-read publication for researchers, practitioners, and leaders who want to make their business part of the conscious future in labor in the environment of digitalization.

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Bianca Ifeoma Chigbu, Sicelo Leonard Makapela
Journal of Open Innovation Technology Market and Complexity.
Published: 2025-09-25
From feed: (TITLE(ai PRE/3 sustainability))

Industry 5.0, Education 5.0, and Work 5.0 are often discussed in silos; this integrative review shows how they cohere into a human-centric, sustainability-aligned model of AI-enabled transformation. This study reviews 200 records (62 included) from Scopus, Web of Science, Google Scholar, and policy portals (EC, UNESCO, ILO, WEF) to examine how these three domains intersect as a unified paradigm. Using an integrative literature review with quality appraisal of empirical studies and policy reports, we identify three cross-domain themes: (1) human-AI collaboration that reorients automation from substitution to augmentation, (2) skills development pathways led by Education 5.0 that bridge education, industry, and labor, and (3) sustainability as a unifying normative anchor across ecological, social, and economic dimensions. Findings show benefits (ergonomics, curriculum-industry alignment, green jobs) and risks (deskilling, inequality, rebound effects). The framework contributes by clarifying shared mediators (skills, ethics, sustainability), surfacing contradictions (automation vs. inclusion), and proposing governance conditions for human-centric outcomes. Major implications include aligning innovation policy, curriculum reform, and labour protections under SDG-coherent strategies, and piloting metrics that capture well-being, equity, and productivity. • Explores Industry 5.0, Education 5.0, and Work 5.0 as a unified digital paradigm • Maps AI in education, human-centric design, and future of work strategies • Addresses skills gap through curriculum reform and upskilling policies • Links sustainability goals to education, labor, and industrial innovation • Proposes a framework for human-AI collaboration in Industry 5.0 systems

Source | Open access

A.V. Senthil Kumar, Ankita Chaturvedi, ATUL BANSAL, Rohaya Latip
Using AI to Develop Sustainability Strategies for A Changing Global Economy
Published: 2025-09-15
From feed: (TITLE(ai PRE/3 sustainability))

written by A.V. Senthil Kumar, Ankita Chaturvedi, ATUL BANSAL, Rohaya Latip Published by Using AI to Develop Sustainability Strategies for A Changing Global Economy

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Gaganpreet Kaur, Swati Malik, Kiran Deep Singh, Sunil Khullar, A. V. Senthil Kumar, Jatin Arora
Using AI to Develop Sustainability Strategies for A Changing Global Economy
Published: 2025-09-15
From feed: (TITLE(ai PRE/3 sustainability))

Current developments in long-term sustainability and artificial intelligence (AI) show an increasing recognition of AI's potential to make a significant contribution to the resolution of sustainability issues. AI has been used more and more in sustainable energy systems, environmental modelling, and environmental management. A rising number of people are also interested in applying AI to make manufacturing processes, transportation networks, and urban planning more effective and sustainable. Employing AI to improve the accuracy and rapidity of forecasting and simulation is an important domain of research. In line with the forecasts, AI will grow more and more indispensable for sustainability. New applications and various new enhancements are enabling the world to address some of the most serious environmental concerns it currently faces as technology becomes ever more advanced. Technology is no doubt essential for ensuring that AI is developed and used in a responsible and ethical manner, and helps society as a whole, doesn’t amp up already-existing unfairness, and doesn’t harm the environment. The aim of this is to present the most recent advancements in AI and sustainability in the modern world.

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Suvarshitha Pusuluru, Karrun Velmurugan, Sai Kumar Punna, Madhumita Ravikumar, Venkatesh Kannan M, Mohanraj Kumar, Hanadi A. Almukhlifi, Farhan R. Khan, Ali Hazazi, Farid Menaa
Biomass and Bioenergy.
Published: 2025-09-13
From feed: (TITLE(ai PRE/3 sustainability))

The growing global energy demand and the need for sustainable alternatives to fossil fuels have accelerated the development of algal biorefineries for biofuel production. Algal polysaccharides, such as cellulose and hemicellulose, offer high-potential feedstocks due to their rich carbohydrate content. Recent advances in pretreatment methods, including deep eutectic solvents and physicochemical techniques, have improved enzymatic hydrolysis, resulting in higher conversion rates and enhanced fermentation efficiency. These improvements have significantly increased biofuel yields while supporting environmental sustainability. Artificial intelligence (AI) and machine learning (ML) are transforming algal bioprocess optimization. Predictive modeling, artificial neural networks, and evolutionary algorithms such as genetic algorithms, fuzzy logic, and particle swarm optimization contribute to enzyme design, fermentation control, cost reduction, and process scalability. In parallel, technoeconomic and environmental assessments, particularly life cycle assessment (LCA), are vital for evaluating resource efficiency, greenhouse gas emissions, and long-term sustainability. This review highlights advancements in polysaccharide utilization, AI-driven innovations, and sustainability frameworks, positioning algal biorefineries as key enablers of global energy security and the transition to a circular bioeconomy. • Algal polysaccharides are carbohydrate-rich feedstocks enabling efficient biofuel production. • Deep eutectic solvent pretreatments boost enzymatic hydrolysis and fermentation yields. • AI-driven tools optimize microbes, enzymes, and scalability for cost-effective biofuels. • Life cycle assessment ensures sustainable biofuels by tracking energy, emissions, and costs. • AI, renewables, and waste integration advance biofuel yields while reducing environmental impact.

Source | Open access

Sinh Duc Hoang, Minh-Trí Hà
Journal of Intellectual Capital
Published: 2025-09-02
From feed: (TITLE(ai PRE/3 sustainability))

Purpose This study aims to investigate pathways to enhancing sustainable organizational performance (SOP) by fostering green intellectual capital (GIC) through the integration of artificial intelligence (AI) and green human resource management (GHRM). Design/methodology/approach This study uses a quantitative research design with survey data from 731 employees and 110 direct managers of information technology firms in Vietnam. Data were collected in two waves via LinkedIn to reduce common method bias and improve response quality. Analysis was conducted using multigroup structural equation modelling (SEM) in R with the lavaan package. Findings The study shows that the relationship between GHRM and SOP is mediated by all three components of GIC: green human capital, green structural capital, and green relational capital. Furthermore, AI is found to negatively impact green structural capital, although this effect is reduced with higher levels of GHRM. Originality/value The study contributes to existing literature on GHRM by considering a potential mediating role of GIC in the relationship between GHRM and SOP. The research further extends this view to include AI’s potential moderating effect on the relationship between GHRM and the individual components of GIC. The findings offer insight into why AI may fail to translate into more favourable green outcomes. While previous research has investigated the direct effects of AI, this is the first study to explore the moderating role of AI in the GHRM–GIC relationship.

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Ilyas Ahmad Huqqani, Mohd Amirul Mahamud, Mohd Azmeer Abu Bakar, Muhammad Wafiy Adli Ramli, Mou Leong Tan, Narimah Samat
AI Driven Strategies for Inclusive and Sustainable Urbanization
Published: 2025-08-27
From feed: (TITLE(ai PRE/3 sustainability))

Urban areas encounter serious environmental issues, such as pollution, resource depletion, waste buildup, and climate change effects, caused by rapid urban growth and increasing populations. Addressing these environmental issues is important for achieving sustainable urban development. Artificial intelligence (AI) provides new, data-based solutions to tackle these problems and encourage environmental sustainability. AI is key in improving energy management with smart grids, cutting emissions using smart transportation, refining waste management, and aiding sustainable city planning through predictive modeling. This topic explores the role of AI in promoting environmental sustainability in urban areas. Furthermore, this topic also discussed how AI can aid in the evolution of urban regions that are not only more ecologically sustainable, such as by enhancing air and water quality assessment and refining waste management practices, but also incorporate greater green infrastructure within the constructed environment and are consequently capable of real-time responses to their interactions with the natural ecosystem.

Source

Magnus Penker, Soo Beng Khoh
AI Si 2025 IEEE International Conference on Artificial Intelligence for Sustainable Innovation Shaping the Future with Intelligent Solutions
Published: 2025-08-26
From feed: (TITLE(ai PRE/3 sustainability))

Achieving Net Zero by 2050 is one of the most pressing challenges of our time. Many organisations struggle to move beyond environmental, social, and governance (ESG) compliance towards embedding sustainability as the way of doing business in the future. This paper introduces a novel, AI-enabled 5-Step G.R.E.E.N. Framework designed to guide organisations from sustainability intent to measurable impact. It demonstrates how sustainability-oriented innovation projects can naturally evolve into a comprehensive innovation management system that is ISO 56001 compatible. The incorporation of agentic artificial intelligence (AI) for innovation management, sustainability science, and systems thinking was demonstrated using two AI agents, namely Agent360 and GREEN Agent. These AI agents enable continuous guidance, benchmarking, and ideation support, helping organisations accelerate their sustainability transformation and innovation outcomes. Together, they demonstrate how the fusion of structured frameworks and intelligent agents can catalyse purpose-driven innovation and unlock strategic pathways to a decarbonised future.

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A. K. Sharadhi, Samuel Kajama Tshikuta
Navigating Responsible Business Practices Through Ethical AI
Published: 2025-08-15
From feed: (TITLE(ai PRE/3 sustainability))

Since the Industrial Revolution, technological advances have molded humanity's identity, workforce, and economic value. Cyberpsychology's Psychological Adaptation Vectors (PAVs) can analyze AI's significant impact on society and human psychology across various domains, from theoretical frameworks to its recent daily-life integration, influencing human cognition and behavior. The current Artificial Generative Intelligence (AGI) and ambient systems leading towards super intelligence, caused by advancements in big data and computations, raise challenges against human uniqueness, employment, identity, and existential queries about the future. As evidenced by Reddit sentiment analysis, AI deployments lack transparency and answerability, aggravating anxiety because they are seen as tools for automation rather than augmentation. This tragedy is creating a Turing Trap, benefiting only the technological class, which can lead to economic inequality. Government intervention, collaboration, and regulation are important to ensure responsible AI development to avoid creating a dystopian society.

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Viachaslau Filimonau, Mark Ashton, Belén Derqui, Gilda Hernández-Maskivker
Sustainable Development.
Published: 2025-08-09
From feed: (TITLE(ai PRE/3 sustainability))

ABSTRACT The hospitality industry, encompassing operations from hotels and resorts to restaurants and event venues, faces increasing pressure to integrate sustainability into its core business practices. The rapid proliferation of artificial intelligence (AI) offers potential to make the industry more sustainable, but this potential remains empirically underexplored. This study bridges this knowledge gap by introducing the convergence innovation framework, a concept defining the fusion of distinct domains to create novel solutions, to examine the integration of AI within hospitality operations for enhanced sustainability across environmental, social, and economic dimensions. Through semi‐structured interviews with 35 senior industry professionals in the United Kingdom and Spain, the study reveals that, beyond well‐known efficiency gains, AI can mitigate environmental impact through proactive optimisation of energy and water consumption, dynamic waste minimisation systems, and intelligent building management that adapts to real‐time conditions. AI can improve social sustainability by personalising guest experiences tailored to eco‐friendly preferences and enhancing staff well‐being through optimised operational tasks. Economically, AI holds opportunities for precision in supply chain management and demand forecasting, leading to waste reduction and cost savings. These findings offer empirically grounded insights for hospitality organisations to strategically capitalise on the convergence of AI and sustainability, promoting resilience and competitive advantage in a rapidly evolving hospitality market.

Source | Open access

Thomas Tongxin Li, Ru‐Ze Liang, Yitong Shang, Cynthia Xin Ding, Yingying Hua, Zhenghao Wang, Mohannad Alhazmi
Sustainable Energy Technologies and Assessments
Published: 2025-08-08
From feed: (TITLE(ai PRE/3 sustainability))

written by Thomas Tongxin Li, Ru‐Ze Liang, Yitong Shang, Cynthia Xin Ding, Yingying Hua, Zhenghao Wang, Mohannad Alhazmi Published by Sustainable Energy Technologies and Assessments

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Carolyn Cole, Arash Hajikhani, Eveliina Hylkilä, Essi Paronen, Hanna Pihkola
International Journal of Life Cycle Assessment.
Published: 2025-08-08
From feed: (TITLE(ai PRE/3 sustainability))

Abstract Purpose As the importance of social responsibility is increasingly recognized, demand for robust social life cycle assessment (S-LCA) has increased. This study presents a novel approach leveraging artificial intelligence (AI) to augment expert-led processes and enhance the efficiency, scalability, and accuracy of S-LCA. Methods The method utilizes advanced natural language processing (NLP) capabilities and large language models (LLMs) to partially automate the evaluation of social factors such as community engagement, labor practices, and human rights considerations against S-LCA pre-defined criteria. The method is applied in parallel to a standard manual practice of a reference-scale S-LCA on a carton product case study in Finland. Results obtained from the AI-augmented assessments are then compared with those derived from the manual method. Results and discussion The comparative analysis reveals a 50% agreement rate between the AI and manual assessment outcomes. We find that three driving factors explain the differences in the remaining outcomes. First, outcomes differed where the human evaluators drew on tacit knowledge unavailable to the AI. Second, the human evaluators inherently weigh negative evidence more heavily than positive evidence. And third, outcomes differed in cases where the assessment of a topic was highly sensitive to stakeholder perspective, and the human and AI evaluators assumed differing perspectives in their assessments. Depending on the factor of difference, in some cases, the AI provided a more objective and fair assessment than the human evaluator, while in others, the human evaluator provided a more contextualized and nuanced assessment than the AI. Conclusions This study contributes to the emerging field of AI-supported assessments by presenting a practical framework for integrating LLMs into S-LCA. The findings aim to inform stakeholders, researchers, and policymakers about the potential benefits and limitations of incorporating AI in evaluation processes that traditionally entail a degree of subjective judgement. The insights gained from the comparative analysis provide valuable considerations for the ongoing development and adoption of AI-assisted approaches in S-LCA and similar evaluation contexts. Recommendations For future applications of automation in S-LCAs and similar evaluation contexts, we suggest mitigations to minimize differing factors, including priming the AI pipeline with contextual materials and explicitly defining desired nuances in system and task instructions.

Source | Open access

Saira Muzafar, Fathimathul Rajeena P.P, Tehmina Karamat Ullah Khan
2025 International Conference on Emerging Trends in Networks and Computer Communications Etncc 2025 Proceedings
Published: 2025-08-05
From feed: (TITLE(ai PRE/3 sustainability))

The availability of affordable clean energy serves as a fundamental requirement for economic expansion and poverty elimination and climate change prevention. Despite industrial revolution and technical advancement still billions of people rely on harmful biomass fuels, and 770 million people have no access to electricity. With the increase in population, limited resources and for the sake of sustainability, the current energy systems need to be refurbished to fulfil the demand of clean and affordable energy. This research discusses the significance of Artificial Intelligence (AI) in reshaping the energy industry to increase renewable energy production and enhance storage mechanism, intelligent grid control to align with United Nations Sustainable Development Goal 7 (SDG 7).

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Norliza Katuk, Noradila Nordin, Adib Habbal
From Smart Cities to the Metaverse A Journey Through Cybersecurity AI and Urban Sustainability
Published: 2025-08-04
From feed: (TITLE(ai PRE/3 sustainability))

This book offers a comprehensive exploration of the convergence between smart city infrastructure and the emerging metaverse. This book highlights the importance of integrating advanced technologies such as artificial intelligence, virtual reality/augmented reality, and blockchain to enhance urban living experiences while addressing such innovations’ security and ethical challenges. Its applications span urban planning, transportation, education, historic preservation, and inclusive city development, making it an essential resource for modern urban development. The book covers many key areas critical to understanding and implementing smart cities and the metaverse. It starts with both domains’ foundational concepts and technological underpinnings, followed by a deep dive into the security infrastructure and challenges smart cities face. Cybersecurity is given special attention, exploring motives and methods of cyberattacks and proposing mitigation techniques and best practices. The book also examines AI chatbots, intelligent transportation, and the integration of digital twins, providing practical case studies and insights. Furthermore, it addresses the socioeconomic implications, governance, and ethical considerations, ensuring a holistic approach to the subject. The motivation for writing this book stems from the contributors’ recognition of the transformative potential of smart cities and the metaverse in creating sustainable, efficient, and inclusive urban environments. By bridging the gap between theoretical research and practical application, the contributors aim to equip researchers, policymakers, and technologists with the knowledge and tools needed to navigate and shape the future of urban living in a digitally interconnected world.

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Tasneem Sultana, P. Venkata Ramchandra Rao, C. Fowmina, V. Vaissnave, T. Ragupathi, P. Selvakumar, T. C. Manjunath
Building AI Driven Decision Making Competencies for Sustainability
Published: 2025-08-01
From feed: (TITLE(ai PRE/3 sustainability))

Artificial Intelligence (AI) is rapidly transforming the global landscape, offering transformative potential to address some of the most pressing challenges of sustainable development. By harnessing the power of intelligent algorithms, machine learning, big data analytics, and automation, AI has emerged as a powerful enabler in achieving the United Nations' Sustainable Development Goals (SDGs). From eradicating poverty and hunger to improving healthcare, education, clean energy access, sustainable cities, climate action, and responsible consumption, AI is driving innovation and efficiency in ways previously unimaginable. Its ability to process vast and complex datasets in real-time enables more informed decision-making, predictive capabilities, and optimization of resource allocation, making it a critical tool in designing and implementing effective, data-driven sustainability strategies. AI's role in promoting good health and well-being—one of the core SDGs—is also noteworthy.

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Mostafa Al‐Emran, Bassam Abu-Hijleh, AbdulRahman A. Alsewari
Education and Information Technologies.
Published: 2024-12-03
From feed: (TITLE(ai PRE/3 sustainability))

written by Mostafa Al‐Emran, Bassam Abu-Hijleh, AbdulRahman A. Alsewari Published by Education and Information Technologies.

Source | Open access

Lotta Hultin, Magnus Mähring
Information and Organization.
Published: 2025-07-31
From feed: (TITLE(ai PRE/3 sustainability))

While the use of Artificial Intelligence (AI) in sustainability efforts continues to grow, dominant approaches remain narrowly focused on optimization, prediction, and control. This paper challenges the predictive/optimizing paradigm by proposing a relational perspective on AI—one that treats uncertainty not as a problem to eliminate, but as a generative space for creativity, care, and transformation. Drawing on theories of relational agency, imagination, and hope, we explore how AI can participate in co-creating more ethical, empathetic, and ecologically attuned practices. Leveraging a preexisting case of AI-driven wildlife management in India, we conduct an analysis of a possible and desirable future, demonstrating how AI's affordances might be reconfigured and expanded: from tools of surveillance and efficiency to invitations for listening, attunement, and world-making. In this reimagined mode, AI supports not only the processing of data but the emergence of stories—enabling practitioners to sense, interpret, and respond to ecological entanglements in ways that foreground more-than-human perspectives and collective vulnerability. We contribute to the growing discourse on sustainable AI by theorizing how practices of imagination and hope can cultivate response-able agency—a form of ethical responsiveness grounded in interdependence rather than mastery. Ultimately, we call for a reorientation of AI design and governance toward practices that do not merely optimize what is, but help bring forth what could be . • We call for moving beyond the dominant predictive/optimizing paradigm on AI for sustainability, and propose a relational perspective on AI that treats uncertainty not as a problem to eliminate, but as a generative space for creativity, care, and transformation. • Building on an existing case of the use of AI in wildlife management in India, we conduct an analysis of a possible and desirable future, demonstrating how AI’s affordances might be reconfigured and expanded: from tools of surveillance and efficiency to invitations for listening, attunement, and world-making • Employing imagination allows us to transcend the limitations of the present, inviting us to co-create with AI and the environment, and cultivating hope opens up space for acknowledging the inherent uncertainty and indeterminacy of the future. Together with ethical responsiveness, this allows for using AI to go beyond incremental solutions, and toward desirable futures, in addressing sustainability challenges • We contribute to the growing discourse on sustainable AI by theorizing how practices of imagination and hope can cultivate response-able agency—a form of ethical responsiveness grounded in interdependence rather than mastery.

Source | Open access

Sourojeet Chakraborty, Berhane Bein Sertu, Daniela Galatro
Proceedings of the Canadian Engineering Education Association Conference.
Published: 2025-07-31
From feed: (TITLE(ai PRE/3 sustainability))

Sustainability-centered engineering is an attribute of Society 5.0, which, in turn, impacts Industry 5.0 (I.D. 5.0) requirements and Education 5.0 (E.D. 5.0) skills within STEM. With the ever-changing global industry landscape, it is imperative to map critically and comprehensively assess (i) the impact of sustainability on E.D. 5.0/I.D. 5.0, (ii) the impact(s) of AI on sustainability measures between the Global North and the Global South economic blocs, and (iii) how these initiatives measures impact Higher Education Institutes (HEIs) in STEM teaching/research. To obtain these perspectives, a bibliometric analysis was performed (2014-2024) on Scopus, based on Research Questions (RQs) from the literature, to capture (i) the existence of an AI gap between the Global North and South, (ii) a lack of current initiatives in sustainability in HEIs in STEM, (iii) an imminent need to promote sustainability-oriented curricula design across HEIs, and (iv) identify novel pedagogical strategies to foster such targeted learning.

Source | Open access

Published by Responsible Innovation in Smart Healthcare AI Iot and Ethical Sustainability Practices We didn't find an OA link, try to find a OA version on Google Scholar
Responsible Innovation in Smart Healthcare AI Iot and Ethical Sustainability Practices
Published: 2025-07-25
From feed: (TITLE(ai PRE/3 sustainability))

written by Published by Responsible Innovation in Smart Healthcare AI Iot and Ethical Sustainability Practices

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R. Rajesh, S Veena, A. Shanthini, D. S. John Deva Prasanna, Yasmeen, P. Selvakumar, T. C. Manjunath
Responsible Innovation in Smart Healthcare AI Iot and Ethical Sustainability Practices
Published: 2025-07-25
From feed: (TITLE(ai PRE/3 sustainability))

When applied to healthcare, predictive sustainability refers to leveraging AI technologies to forecast, analyse, and optimize healthcare systems to balance long-term environmental, economic, and social considerations. AI particularly offers substantial benefits by helping healthcare organizations anticipate challenges and optimize resources while reducing their environmental footprint and ensuring better care for populations. In this comprehensive analysis, we explore the significance of AI in predictive sustainability within healthcare, focusing on its role in improving efficiency, managing resources, and advancing global health goals. In the healthcare sector, AI's role in improving operational efficiency is critical for predictive sustainability. Healthcare systems worldwide face significant challenges, including rising patient demands, resource constraints, and increasing costs. Predictive analytics, powered by AI, allows healthcare administrators to anticipate future trends.

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R. Velmurugan, J. Sudarvel, R. Bhuvaneswari, Ravi Thirumalaisamy
AI Driven Solutions for Solar Energy Efficiency Irradiance Modeling and Pv Forecasting
Published: 2025-07-18
From feed: (TITLE(ai PRE/3 sustainability))

The need to stabilize the grid is only increasing as global demand for energy appreciates as countries urbanize, Digital Transformation and populated by the Electric Vehicles. Traditional grids suffer from heartbleed. Demand forecasting, load balancing, and real time decision gets improved by use of AI in the energy systems. In this, how AI models take in huge datasets, utilizing machine learning algorithms to try and gauge energy needs and consequently how to versus multiply power appropriation. Such models increase the grid flexibility, support integration of renewable energy, as well as the grid efficiency. It also allows predictive maintenance, improves downtime, and manages distributed power generation. The utilization of AI in reducing carbon emissions is mainly regarding cybersecurity and data privacy.

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K. R. Senthilkumar, R. Shantha Selva Kumari, A. Senthamizhselvi, Rakesh Kumar
Smart Sustainability the Role of AI in Business Intelligence
Published: 2025-07-16
From feed: (TITLE(ai PRE/3 sustainability))

Artificial intelligence (AI) technology changes banking operations through its application in financial management by strengthening risk evaluation and decision‐making processes while improving financial transparency. The research investigates how AI financial strategies affect sustainable financial performance (SFP) through the mediating influence of financial transparency and governance (FTG). Researchers used structural equation modeling (SEM) in AMOS to analyze responses from 600 banking professionals employed at five private-sector banks located in Tamil Nadu. The study shows that AI‐based financial forecasting (AFF), automated decision‐making (ADM), and AI‐driven risk assessment (ARA) improve sustainable financial performance (SFP) through full mediation by financial transparency and governance (FTG). Sustainable credit scoring (SCS) failed to produce significant results, which indicates limitations within current AI‐based credit models. The results indicate that strong AI governance structures are essential to fully harness financial benefits from AI technologies. The research provides managerial insights and recommends more investigation into the extended financial effects of AI adoption.

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Neeraj Gupta, Debapriya Samal, Sunita Jatav, Priti Aggarwal
Smart Sustainability the Role of AI in Business Intelligence
Published: 2025-07-16
From feed: (TITLE(ai PRE/3 sustainability))

Artificial intelligence (AI) in sustainability reporting is currently transforming corporate governance in today's business environment. With pressure from investors and regulators to become more transparent with their environmental, social, and governance (ESG) reporting, companies across the globe are using AI to inscribe sustainability into operations that are transparent, efficient, and effective. In this chapter, the key role played by AI in shifting the paradigm of sustainability reporting is highlighted, i.e., the automation of data collection, the usage of huge, real, useful, and action-driven data to move decision-making forward. It explains how AI tools and technologies such as machine learning, natural language processing, and predictive analytics can be leveraged to provide added value to ESG reporting, suggest new measures towards sustainability, and facilitate stakeholder engagement through user interactive and responsive platforms. However, the arrival of AI in sustainability reporting at the same time also faces certain difficulties. The chapter also addresses some of the key governance concerns, including ethical concerns, quality of data, risk management, and disclosure of AI systems. In addition, the study on regulatory evolution, emerging technologies, and the actors’ viewpoint on the development of the future of AI-enriched sustainability reporting is also proposed. This chapter, by offering an integrative model for the responsible use of AI in corporate governance (CG), can contribute valuable information to business managers, professionals in corporate sustainability, and legislators who wish to harness the benefits of AI while being mindful of ethical, transparent, and efficient sustainability applications. What makes this work innovative is the interdisciplinary approach (AI and sustainability reporting, corporate governance, and so on) adopted in the present study in an attempt to offer hands-on solutions for companies who want to make measurable long-term value.

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Rahmawati Rahmawati, Rismawati Rismawati, Duriani Duriani, Yunus Yunus
AI Policy and the Future of Human Centered Education
Published: 2025-07-16
From feed: (TITLE(ai PRE/3 sustainability))

This chapter examines various funding strategies crucial for the sustainable incorporation of Artificial Intelligence (AI) in education. While AI revolutionizes education via adaptive systems, data analytics, and personalized learning tools, sustainable financing continues to be a significant obstacle. The study examines conventional funding sources including government grants and philanthropy, alongside innovative forms such as venture capital, social impact bonds, and subscription-based frameworks. Case studies demonstrate both successful and unsuccessful AI applications, emphasizing lessons about stakeholder participation, equity, and quantifiable impact. Focus is directed towards synchronizing financial strategies with educational objectives, utilizing data to illustrate value, and establishing partnerships for sustained viability. The results support the implementation of innovative, inclusive, and performance-oriented funding mechanisms to guarantee that AI technologies improve educational outcomes broadly.

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Faizan ul Haq, Norazah Mohd Sukı, Made Setini, Asim Masood, Talal Khan
Sustainable Futures.
Published: 2025-07-15
From feed: (TITLE(ai PRE/3 sustainability))

Green Artificial Intelligence (Green AI) presents a strategic pathway for small and medium-sized enterprises (SMEs) in emerging economies to enhance sustainability by optimizing energy use, minimizing waste, and fostering eco-innovation. Despite its potential, adoption remains limited due to financial constraints, technological readiness gaps, and weak institutional support. This study investigates the drivers of Green AI adoption and its impact on sustainable performance within the integrated framework of Technology-Organization-Environment (TOE) and the Technology Acceptance Model (TAM), incorporating Green Investment (GI) as a mediator and Green Servant Leadership (GSL) as a moderator. Survey data from 399 manufacturing SMEs in Pakistan were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The results reveal that perceived ease of use, perceived usefulness, and organizational readiness significantly influence Green AI adoption, which in turn enhances both environmental and operational performance. GI strengthens this relationship by addressing resource barriers, while GSL moderates the adoption process by fostering leadership commitment to sustainability. By extending the TOE-TAM model, this study contributes new theoretical insights into sustainable technology adoption and offers practical recommendations for policymakers and SME leaders to support Green AI implementation in resource-constrained contexts.

Source | Open access

Kyvalya Garikapati, Jorge Isaac Torres Manrique, Rahul Goyal
Revista Oficial Del Poder Judicial.
Published: 2025-07-15
From feed: (TITLE(ai PRE/3 sustainability))

La inteligencia artificial (IA) en la India tiene el potencial de desempeñar un papel clave para garantizar la sostenibilidad y el desarrollo sustentable. La aplicación de la IA puede: (i) acelerar las prácticas empresariales sostenibles y la transición energética, ayudaría así a reducir el impacto ambiental; (ii) mejorar la eficiencia en la gestión de los recursos naturales, como el agua y los fertilizantes en la agricultura, por lo que aportaría a minimizar el impacto ambiental; (iii) tener un impacto positivo en la predicción y la gestión de desastres naturales, como terremotos e inundaciones, con lo cual permitiría una respuesta más rápida y eficiente; (iv) utilizarse para identificar y controlar especies en peligro de extinción, apoyaría de este modo la conservación de la biodiversidad. A su vez, la IA en el Perú puede: (i) acelerar las prácticas empresariales sostenibles y la transición energética, por lo cual ayudaría a reducir el impacto ambiental; (ii) utilizarse en la gestión eficiente de los recursos naturales, como el agua y los fertilizantes en la agricultura, de forma que ayudaría a minimizar el impacto ambiental; (iii) tener un impacto positivo en la movilidad sostenible y el desarrollo de ciudades inteligentes en Perú, contribuiría así a resolver los desafíos ambientales que enfrentamos. También es importante señalar que la gobernanza aplicada a la inteligencia artificial tiene el potencial de jugar un papel muy importante en el desarrollo de ambos países, por lo que su implementación debe ir acompañada de políticas y principios sólidos que garanticen su uso ético y responsable para minimizar riesgos y daños. En este artículo, los autores desmenuzan esta importante cuestión, analizando sus impactos, sus ventajas, sus desventajas, sus diferencias y sus similitudes, con el fin de destacar las importantes lecciones aprendidas de esta experiencia comparada, añaden a su estudio el enfoque desde la perspectiva de los derechos fundamentales y la interdisciplinariedad.

Source | Open access

Bashir Ahmad, Imran Shafique, Masood Nawaz Kalyar
International Journal of Innovation.
Published: 2025-07-08
From feed: (TITLE(ai PRE/3 sustainability))

Purpose – This study aims to investigate the interplay between AI-capabilities and AI oriented leadership in eliciting sustainable performance. Specifically, this study submits that AI capabilities influence frugal innovation which in turn promotes firm sustainable performance. Moreover, the said relationship is moderated by AI-oriented leadership. Design/methodology/approach – This study used survey method to collect data from 162 manufacturing SMEs. The data was analyzed using PLS-SEM to test the hypotheses. Findings – Results indicate that AI-capabilities are important source of firm sustainability performance where it determines sustainability performance directly as well as through frugal innovation. The mediating mechanism offers the insights into how AI capabilities can be translated into overall organizational gains. The recognition of frugal innovation as an important mediator offers a comprehensive insight of the mechanisms through which AI capabilities affect firm sustainable performance. In addition, AI-oriented leadership was found to have positive moderating effect for the relationship between AI-capabilities and frugal innovation. Results also support the moderated mediation effect suggesting that AI-oriented leaders who can successfully utilize AI resources and enhance an innovative and creative culture have greater likelihood of optimum exploiting of AI-capabilities in achieving sustainable performance. Originality/value – Considering the importance of sustainable performance, this study is the first study that examines the direct and indirect effect AI capabilities on sustainable performances.

Source | Open access

Geeho Jeon
Business Strategy and Development
Published: 2025-07-06
From feed: (TITLE(ai PRE/3 sustainability))

ABSTRACT This study examines the interplay between national AI capabilities, measured by the Oxford (161 countries), Tortoise (81 countries), and Stanford (36 countries) AI Indices, and Environmental, Social, and Governance (ESG) performance, advancing international business (IB) and sustainability scholarship. We propose AI‐enabled ESG capability—a country‐level construct integrating AI resources within institutional contexts—as a novel framework, synthesizing Resource‐Based View, Institutional Theory, and Knowledge‐Based View to address tensions between regulatory pressures and technological advantages. Employing Pearson correlation, hierarchical regression, and K ‐means clustering, we analyze AI pillars' influence on ESG, controlling for political stability, GDP per capita, internet penetration, and population size. Findings highlight Data and Infrastructure (Oxford, R 2 = 0.916), Talent (Tortoise, R 2 = 0.936), and Political Stability (Stanford, R 2 = 0.850) as primary drivers, with stronger effects in developed economies. Clustering reveals trade‐offs: AI‐dominant nations lag in ESG, while ESG‐strong countries underutilize AI. These insights extend IB theories and guide multinational enterprises (MNEs) and policymakers in aligning AI with ESG through robust governance, digital infrastructure, and STEM education, fostering sustainable global value chains.

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Amelia Lee Doğan, Hongjin Lin, Lindah Kotut
DIS 2025 Proceedings of the 2025 ACM Designing Interactive Systems Conference.
Published: 2025-07-04
From feed: (TITLE(ai PRE/3 sustainability))

As the Earth's temperature continues to rise, increasing investments are being made to develop artificial intelligence (AI) technologies to address the current climate crisis.Through interviewing 19 participants-comprising climate and environmental advocates and developers of AI for sustainability in the US and Canada-we examine how advocates perceive and use these technologies, and how their perspectives converge and diverge from practitioners developing AI for sustainability.We identified three key findings: 1) while approaches differ, developers and advocates expressed care for people and the planet; 2) the developers' and advocates' values and perceptions of AI technology varied, especially around ethical issues; and 3) developers and advocates had distinct approaches to using and designing AI and digital tools.Our findings, guided by a climate justice lens, underscore the need for decision-makers to: engage with advocates from intended beneficiary communities early in the design process; prioritize the urgency of the climate crisis; and emphasize the tangible environmental and societal impact of digital systems.

Source | Open access

Réka Koteczki, Dániel Csikor, Boglárka Eisinger Balassa
Discover Sustainability.
Published: 2025-07-03
From feed: (TITLE(ai PRE/3 sustainability))

Abstract Generative artificial intelligence (GAI) is becoming increasingly important in business processes, including human resource management. GAI can offer the potential to automate repetitive tasks in recruitment processes, optimise decision making, and reduce administrative burdens. Although AI can help increase operational efficiency, environmental pressures must also be taken into account. AI models require significant computing power, resulting in high energy consumption and increased CO 2 emissions. This dichotomy may raise the question of whether the efficiency gains provided by GAI outweigh the environmental burden. This article examines the environmental impacts of GAI on HRM through a case study. The research combines qualitative and quantitative methods: expert interviews are used to explore practical applications, while calculations on energy consumption, costs, and emissions are carried out by comparing traditional and AI-based recruitment methods. The results of the case study showed that the integration of GAI led to efficiency gains. The time required for the recruitment process was reduced by 13.25 h, which could save thousands of man-hours per year. At the same time, costs and energy consumption and associated carbon emissions were reduced. The study highlights the duality of “AI for sustainability” and “sustainability of AI”, highlighting that while GAI can contribute to more sustainable corporate operations, its own environmental footprint raises questions about long-term sustainability. The results will provide HR professionals, decision makers, and organisations with practical insights into the potential for sustainable use of AI.

Source | Open access

Robin Kunju Mol Raj, Marek Vochоzka, S Sujatha
Discover Sustainability.
Published: 2025-07-01
From feed: (TITLE(ai PRE/3 sustainability))

Media play an essential role in shaping public attitudes, behaviors, and long-term societal developments. Although AI is often addressed in the context of technical advancement, its role in media discourse on Sustainable Development Goal 1 (SDG1 - No Poverty) is underexplored. Media sentiment towards AI and poverty is researched here through sentiment analysis, topic modeling, and predictive modeling of global news headlines on the GDELT database. This study addresses this gap by examining worldwide media sentiment and narrative trends on AI and poverty from 2017 to 2025. Sentiment classification used VADER, TextBlob, and BERT models, with a net positive sentiment reported in AI-poverty stories. Latent Dirichlet Allocation (LDA) topic modeling revealed labor workforce automation and sustainable economic transformation to be the most discussed themes. In addition, Long Short-Term Memory (LSTM) neural networks were employed to predict sentiment trends. The model demonstrated low predictive validity (R² = 0.0124; Pearson correlation = 0.1223), suggesting that additional contextual variables are needed. Notably, BERT performed a more accurate detection of subtle changes in sentiment than VADER and TextBlob, which tended to make more coarse categorizations. The LDA model further revealed consistent discussions surrounding government policy, labor challenges, and ethical concerns about automation. These findings highlight the polarized nature of AI-related media coverage in poverty contexts and the value of sentiment analysis in assessing AI’s perceived socio-economic role. Our study contributes to the literature by combining computational approaches to evaluate sustainability-oriented media narratives.

Source | Open access

Pawan Whig, Shashi Kant Gupta, Rahul Reddy Nadikattu, Pavika Sharma
Artificial Intelligence Driven Models for Environmental Management
Published: 2025-07-01
From feed: (TITLE(ai PRE/3 sustainability))

The application of artificial intelligence (AI) in environmental sustainability is increasingly becoming essential to address global challenges such as climate change, resource management, and pollution control. AI-driven technologies enable more efficient data collection, predictive analytics, and decision-making processes to optimize resource utilization, reduce waste, and enhance environmental monitoring. By integrating AI with environmental sciences, industries can develop sustainable practices, minimize carbon footprints, and manage ecosystems more effectively. This interdisciplinary approach fosters innovation in renewable energy, biodiversity conservation, and urban planning, thus contributing significantly to global sustainability goals. However, ethical considerations and potential unintended consequences of AI in environmental management warrant careful regulation and transparent governance.

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Andri Dayarana K. Silalahi
Technology in Society
Published: 2025-06-23
From feed: (TITLE(ai PRE/3 sustainability))

written by Andri Dayarana K. Silalahi Published by Technology in Society

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Aqsa Anwar, Sanjeev Kumar
Integrating AI and Sustainability in Technical and Vocational Education and Training Tvet
Published: 2025-04-24
From feed: (TITLE(ai PRE/3 sustainability))

This study explored integrating AI-powered tools into TVET curricula and their potential to promote development goals. Incorporating AI can make education more effective and inclusive, address the growing demand for skilled professionals, and promote eco-friendly and socially responsible methods. AI tools can provide personalized educational experiences tailored to student's specific needs and learning preferences, potentially improving educational outcomes and preparing students for evolving workforce demands. This study investigated the current state of AI implementation in TVET, emphasizing its potential advantages, including customized educational experiences, engaging virtual simulations, and enhanced resource management. These technological advancements have the potential to promote more accessible and fair opportunities for high-quality education, foster the acquisition of practical skills with minimal environmental consequences, and decrease TVET institutions' carbon output.

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Tamara Adel Al-maaitah, Khalid Ali Alduneibat, Sajead Mowafaq Alshdaifat, Rakan Alsarayreh, Ahmad Yahiya Ahmad Bani Ahmad, Areej Faeik Hijazin
Heritage and Sustainable Development.
Published: 2025-06-17
From feed: (TITLE(ai PRE/3 sustainability))

This study investigates the influence of AI adoption tools, user readiness, and ease of use on sustainability accounting education in Jordanian public universities, moderated by academic integrity. As AI becomes increasingly integrated into university instruction, its influence on learning outcomes and ethics is paramount. Survey data from 384 instructors at 10 Jordanian public universities were analyzed with Smart PLS. The results show that AI implementation improves education in sustainability accounting by improving accessibility, effectiveness, and personalization. Usability greatly facilitates the adoption of AI, with increased student engagement by providing higher levels of involvement. Yet, as education in academic integrity encourages responsible and ethical AI use, it also creates challenges for adoption whenever regulations are perceived as overly limiting. These results highlight the importance of a balance between innovation and ethics and stress how institutions need to invest in adaptive policy infrastructures and digital literacy. The study contributes to theoretical understanding and practical guidance for policymakers and educators in AI-supported education.

Source | Open access

Priscilla Bahaw, David Forgenie, Ghulfam Sadiq, Satesh Sookhai
Sustainable Futures.
Published: 2025-06-08
From feed: (TITLE(ai PRE/3 sustainability))

Generative AI has emerged as a game-changing technology with vast potential to enhance business sustainability. This study explores the adoption and application of generative AI among small and medium-sized enterprises (SMEs) in a small island developing state. The study utilizes the Technology Acceptance Model (TAM) and the Triple Bottom Line (TBL) framework. It integrates quantitative and qualitative methods to comprehensively understand generative AI's role in fostering sustainable business practices. Quantitative findings reveal that perceived ease of use and usefulness significantly influence SMEs' intentions to adopt generative AI, ultimately predicting its actual usage. Qualitative insights complement these findings by identifying four key applications: operational efficiency, data-driven decision-making, sustainable product and service innovation, and building a sustainable brand identity. Despite its potential, the study acknowledges limitations, including focusing on a single SIDS and relying on self-reported data, which constrain generalizability. However, these limitations do not diminish the study's importance, as it highlights practical pathways for SMEs to overcome resource constraints and achieve sustainability goals. The findings highlight the transformative role of generative AI in equipping SMEs with innovative tools to balance profitability with environmental and social responsibility. Policymakers are urged to support this transition through education and outreach, making generative AI accessible and practical for SMEs.

Source | Open access

Muhammad Ali, Līga Peiseniece, Elīna Miķelsone
Environment Technology Resources Proceedings of the 16th International Scientific and Practical Conference.
Published: 2025-06-08
From feed: (TITLE(ai PRE/3 sustainability))

We examined the role that Green Work Commitment plays in the context of green HR practices, such as green hiring, green compensation, green training, AI components, and organisational sustainability. It is a quantitative research method that uses questionnaires as a data-collecting tool. The study population was the study population of employees associated with multinational corporations and essential sectors in Pakistan, such as Pakistan International Airlines, Procter & Gamble Pakistan, banks, educational institutions, and IT organisations. Likewise, key organisations such as Navoi Mining and Metallurgy Combinat JSC, POSCO International Textile, LUKOIL Uzbekistan Operating Company, Nestlé Uzbekistan, Huawei Uzbekistan, various banks, educational institutions, as well as the IT sector, collected data from Uzbekistan. In both cases, quantitative methods were used, and random and convenience sampling were used to elicit 373 responses. Descriptive statistics were performed in SPSS version 23 (mean, rates, and standard deviation), and inferential statistics in Smart PLS version 3.0. Based on these goals, 12 hypotheses were developed to investigate the relationships between green HR practices and environmental sustainability. Considering these hypotheses, ten had strong backing, while two had less strength.This study adds to the expanding body of information on Green HR practices by emphasising the mediating function of green work commitment in promoting environmental sustainability inside multinational firms in Pakistan. In this study, we outline the path for future research into the broader implications of Green HR practices for sustaining organisations across regions and industries. It would be interesting to study how green HR approaches evolve alongside AI aspects in future research, emphasising how employee engagement and organisational success are impacted by green work commitments. Furthermore, an analysis of how leadership and company culture impact green HR policies may provide more insight. To better understand how these techniques are applicable globally, a comparison can be made between emerging and developing economies.

Source | Open access

Vinit Ghosh, Varun Chotia, Rsha Alghafes, Sarah Basahel, Asif Nazrul
Technological Forecasting and Social Change
Published: 2025-06-02
From feed: (TITLE(ai PRE/3 sustainability))

written by Vinit Ghosh, Varun Chotia, Rsha Alghafes, Sarah Basahel, Asif Nazrul Published by Technological Forecasting and Social Change

Source

Yufan Yang, Chunxiao Li, Zhirui Qu
Journal of Hospitality and Tourism Management
Published: 2025-06-01
From feed: (TITLE(ai PRE/3 sustainability))

written by Yufan Yang, Chunxiao Li, Zhirui Qu Published by Journal of Hospitality and Tourism Management

Source

Mohammad Ishrat, Wasim Khan, Syed Mohd Faisal, Md Shamsul Haque Ansari, Faheem Ahmad
Integrating AI and Sustainability in Technical and Vocational Education and Training Tvet
Published: 2025-04-24
From feed: (TITLE(ai PRE/3 sustainability))

Integrating Artificial Intelligence (AI) and sustainability into Technical and Vocational Education and Training (TVET) is essential for preparing a future-ready workforce. This paper explores the convergence of AI and sustainability in TVET, examining emerging trends, challenges, and strategic approaches. AI technologies, such as machine learning and intelligent tutoring systems, enable personalized learning, while sustainability-driven curricula foster environmental stewardship. Key challenges include the digital divide, curriculum restructuring, and ethical considerations. A strategic framework focusing on curriculum development, technological integration, capacity building, and stakeholder collaboration is proposed to address these challenges. By fostering innovation and promoting lifelong learning, TVET institutions can build a technologically advanced, environmentally responsible global workforce.

Source

Puneet Thapar, Richa Bharti, Jigisha Sharma, N. AMAR, Arpita Sapehia, Ishwar D. Aggarwal, Shailendra Sharma
2025 International Conference on Networks and Cryptology Netcrypt 2025
Published: 2025-05-29
From feed: (TITLE(ai PRE/3 sustainability))

This study shows how AI, sustainability and health-conscious food management can come together to address the global challenge of food waste while providing innovative solutions for healthier and more sustainable management of food at home. The world consumes a large amount of food every year, but a significant portion is wasted, putting pressure on the environment. This problem is serious, yet many people struggle to use the ingredients they have before they spoil. This research paper introduces a web-based application called the Smart AI Food Detector, which allows users to receive personalized recipes based in the ingredients available. By using artificial intelligence, this system ensures that food is used to its fullest potential, promotes responsible consumption and encourages healthy choices. The application not only provides recipes but also offers health insights, informing users whether the meals are suitable for specific health conditions, such as diabetes, hypertension and heart disease. It tailor's recipe suggestions according to user preferences, ingredient availability, nutritional needs and dietary restrictions, thus reducing food waste while promoting healthy eating habits. This research highlights the integration of current approaches to food and health management: AI, sustainability and healthy eating, and how these can be utilized to tackle the problem of food waste without compromising the quality of food and lifestyles.

Source

Sunanda Das
2025 International Conference on Networks and Cryptology Netcrypt 2025
Published: 2025-05-29
From feed: (TITLE(ai PRE/3 sustainability))

The kingdom is changing previous than we continuously hypothetical probable. Powerful original knowhows bear sacking conservatively uncooperative before difficult proceedings, creation a large change in what technique we alive previously effort. Currently is the period meant at skills to revenue a robed lengthy arrival on in what way they're employed formerly in pardon method to industrialized a part of this enormous increase of innovation? A productive occupational strategy often is depending on being a first Digital change through adopters of these new services, which is added goal vital to pay maintenance to innovations parallel artificial intelligence (AI), block chain, as well cyber security. Numerical / digital change ensues the minute alphanumeric data is collective bowed on a industry's goods, dealings, in accumulation products to gain waged proficiency, rally patron facts, expand curved on different markets, then as well realize peril. Convinced corporations power detection it inspiring to bear arithmetical change. However thru method of arithmetical services alike the haze, IoT, AI, big data, also portable continue increasingly used in extra subdivisions of saleable before development, it eats grow robust that digitalization is the only idea that ampule give skills a shy advantage. Sole of the chief interests of relaxed AI is its aptitude to assistance businesses delivers healthier purchaser provision. This software permits manufactures to automate errands analogous replying investigations or responding to comments on shared TV seats similar Facebook besides Twitter. This pays they won't vital by technique of many folks working in sound canters or organization infrastructures afterward patrons announcement up old-fashioned intended at runs who vessel do slightly diverse supplementary inspired via their dated. In today's arithmetical creation, employments recurrently attendance used for behaviours to tradition facts to rally their procurer data. Artificial intelligence (AI) is a convincing tool to provision finished this box. The main goals of this education are to discovery out: tranquil AI destined at improved customer delivery; powerful aide's support nearby bonds former; uniqueness society; clash resolve; documentations party; Designed for this task regular crucial cystography as well as lopsided key cryptography, mess function——these arrangements will be castoff.

Source

Andi Asrifan, Khristianto Khristianto, Nurul Aini, Wahira Wahira
Rethinking Literacy in the Era of Sustainability and Artificial Intelligence
Published: 2025-05-28
From feed: (TITLE(ai PRE/3 sustainability))

The amalgamation of Artificial Intelligence (AI) and sustainability in education fosters a revolutionary paradigm for contemporary literacy. AI-driven education facilitates customized learning, flexible curricula, and smart tutoring, improving student involvement and effectiveness. Simultaneously, sustainability promotes environmental consciousness, ethical choices, and social accountability. Integrating AI with sustainability cultivates students' critical thinking and problem-solving abilities to tackle global issues like climate change and resource management. Nonetheless, issues such as digital disparity, ethical dilemmas, and educator readiness must be confronted. Enacting policies that foster AI literacy, sustainability education, and educator training guarantees responsible AI implementation. This integration enables students to become technologically proficient and ecologically aware, promoting a more inclusive and sustainable educational framework.

Source

Published by Behavioral Finance and AI Tools for Sustainability We didn't find an OA link, try to find a OA version on Google Scholar
Behavioral Finance and AI Tools for Sustainability
Published: 2025-05-21
From feed: (TITLE(ai PRE/3 sustainability))

written by Published by Behavioral Finance and AI Tools for Sustainability

Source

M I Anju, Ishwarya Kothandaraman, R. Arunadevi, K. Rajakumari, K. Fouzia Sulthana, M. Robinson Joel
Citizen Centric Artificial Intelligence for Smart Cities
Published: 2025-05-07
From feed: (TITLE(ai PRE/3 sustainability))

Rapid city expansion presents many obstacles to urban sustainability, such as resource management, pollution, and public safety issues. This study looks at how pollution management and environmental monitoring can be advanced by artificial intelligence (AI) to improve urban sustainability. Artificial Intelligence (AI) technology, including automation, predictive modelling, and real-time data analysis, make it possible to create complex systems that monitor the quality of air and water, identify environmental threats, and maximize resource use. Cities can continually monitor environmental conditions, offering timely insights for interventions and lowering environmental footprints by combining AI-driven sensors and IoT devices. Additionally, AI helps to build sustainable urban infrastructure and promotes public safety by recognizing pollution patterns associated with health hazards. AI has the capacity to completely transform urban sustainability, which emphasizes how crucial it is for technology developers, legislators, and urban planners to work together.

Source

Quanwei Chen, Xin Lai, Yong Zhang, Junjie Chen, Yuejiu Zheng, Xiaolong Song, Xuebing Han, Minggao Ouyang
Carbon Footprints.
Published: 2025-04-28
From feed: (TITLE(ai PRE/3 sustainability))

Lithium-ion batteries (LIBs) are pivotal for electric vehicles and energy storage, yet their sustainability assessment is hindered by methodological limitations. Artificial intelligence (AI) is poised to transform lifecycle assessment (LCA) paradigms for LIBs. This study employs strengths, weaknesses, opportunities and threats analysis to comprehensively examine the role and prospects of AI for LCA in LIBs. The objective is to capitalize on the technology's strengths, mitigate its weaknesses, and identify potential opportunities and threats. Furthermore, this research proposes future studies to enhance the application of AI in the LCA of LIBs, including the establishment of unified standards for data collection, processing, and sharing, improving data transparency, promoting interdisciplinary collaboration, and developing more robust AI models. This study will provide a scientific reference for the research on efficient, scalable, and automated sustainability assessment methods in LIBs, driving the battery and transportation toward more sustainable development.

Source | Open access

Poornima Mahesh
Integrating AI and Sustainability in Technical and Vocational Education and Training Tvet
Published: 2025-04-24
From feed: (TITLE(ai PRE/3 sustainability))

Artificial intelligence (AI) and sustainability in technical and vocational education and training (TVET) looks at methods of technology usage to develop educational strategies that progress AI while working toward sustainable development. The analysis of artificial intelligence (AI) in vocational education and training (TVET) examines ethical elements and essential policy requirements for accomplishing effective deployment. Educational practices increasingly under AI control demand immediate ethical solutions to address data privacy problems and avoid algorithmic bias as well as combat inequalities. The current chapter studies successful implementations of AI integration within vocational learning programs together with approaches which preserve ethical criteria. The chapter recommends working together between educators and policymakers together with industry representatives to optimize the potential value of artificial intelligence in vocational education.

Source

Burak Koçak, Andrea Ponsiglione, Valeria Romeo, Lorenzo Ugga, Merel Huisman, Renato Cuocolo
Insights into Imaging.
Published: 2025-04-17
From feed: (TITLE(ai PRE/3 sustainability))

Artificial intelligence (AI) is transforming radiology by improving diagnostic accuracy, streamlining workflows, and enhancing operational efficiency. However, these advancements come with significant sustainability challenges across environmental, economic, and social dimensions. AI systems, particularly deep learning models, require substantial computational resources, leading to high energy consumption, increased carbon emissions, and hardware waste. Data storage and cloud computing further exacerbate the environmental impact. Economically, the high costs of implementing AI tools often outweigh the demonstrated clinical benefits, raising concerns about their long-term viability and equity in healthcare systems. Socially, AI risks perpetuating healthcare disparities through biases in algorithms and unequal access to technology. On the other hand, AI has the potential to improve sustainability in healthcare by reducing low-value imaging, optimizing resource allocation, and improving energy efficiency in radiology departments. This review addresses the sustainability paradox of AI from a radiological perspective, exploring its environmental footprint, economic feasibility, and social implications. Strategies to mitigate these challenges are also discussed, alongside a call for action and directions for future research. CRITICAL RELEVANCE STATEMENT: By adopting an informed and holistic approach, the radiology community can ensure that AI's benefits are realized responsibly, balancing innovation with sustainability. This effort is essential to align technological advancements with environmental preservation, economic sustainability, and social equity. KEY POINTS: AI has an ambivalent potential, capable of both exacerbating global sustainability issues and offering increased productivity and accessibility. Addressing AI sustainability requires a broad perspective accounting for environmental impact, economic feasibility, and social implications. By embracing the duality of AI, the radiology community can adopt informed strategies at individual, institutional, and collective levels to maximize its benefits while minimizing negative impacts.

Source | Open access

Chandni Bansal
Journal of Strategy and Innovation
Published: 2025-04-16
From feed: (TITLE(ai PRE/3 sustainability))

written by Chandni Bansal Published by Journal of Strategy and Innovation

Source

Ramee RiadHwsein, Mayank Nagar, Dharmapuri Siri, Shital Kewte, T. Kuppuraj, Shukhrat Khudayberganov
2025 International Conference on Metaverse and Current Trends in Computing Icmctc 2025
Published: 2025-04-10
From feed: (TITLE(ai PRE/3 sustainability))

The increasing threats of climate change, deforestation, and biodiversity loss demand innovative technological solutions for real-time environmental monitoring and conservation. The contributions and contributions of this work include an AI-Driven Adaptive Environmental Surveillance System (AESS), with computer vision, edge AI, federated learning, and neuromorphic computing, to autonomously, efficiently, and privacy-protecting environmental surveillance. The system additionally combines multi-modal detection of RGB imaging, infrared, LiDAR and hyperspectral data, raising the detection accuracy. Along with providing a bio-inspired swarm AI drones and ground-based robotic units for real-time ecological parameter assessment of real-time illegalities, their eyes and ears are the swarm AI drones and ground-based robotic units. The adaptive AI models, armed with reinforcement learning, undergo continuous improvement in terms of the detection accuracy owing to the variations in the environment. The framework of the secure, transparent, immutable data storage for conservation data is based on the blockchain technology. The experimental results reveal that automation achieved 95% reduction of manual surveillance, 80% decrease in the response time to illegal activities, 50% energy saving as compared to traditional AI models. This work furthers sustainability of environmental monitoring by producing a more efficient, scalable, and applicable means of using AI. AESS, proposed as a transformative step to autonomous, real-time, environmental protection in the face of critical conservation challenges, is evidence of secure data, energy efficient, with minimal human intervention as possible. Future work extends the real world deployment scenarios, as well as makes AI more adaptable.

Source

Cong Doanh Duong
Sustainable Development.
Published: 2025-04-04
From feed: (TITLE(ai PRE/3 sustainability))

ABSTRACT This study aims to examine how generative artificial intelligence adoption and perceived artificial intelligence capacities influence sustainability‐oriented entrepreneurial intentions through psychological mechanisms, including perceived desirability and feasibility. Despite growing research interest in sustainability‐oriented entrepreneurship, the role of technological enablers, particularly artificial intelligence, in shaping entrepreneurial intentions has been underexplored. To achieve this objective, an advanced approach—polynomial regression with response surface analysis—was employed to test the formulated hypotheses using data from 385 participants. The study further shows that sustainability‐oriented entrepreneurial intentions improve significantly when perceived desirability and feasibility are aligned but remain unaffected by misalignment. Generative artificial intelligence adoption and perceived artificial intelligence capacities are shown to directly and indirectly enhance entrepreneurial intentions through perceived desirability and feasibility, highlighting the dual role of artificial intelligence as a practical enabler and psychological motivator. These findings contribute to the extent of entrepreneurial literature by indicating how artificial intelligence technologies foster sustainability‐oriented entrepreneurship. Moreover, these findings provide valuable insights for policymakers, educators, and organizations by demonstrating how artificial intelligence can be leveraged to promote sustainable innovation and entrepreneurship. By integrating artificial intelligence into entrepreneurial education and policy frameworks, stakeholders can better support the development of sustainability‐oriented entrepreneurs and advance global sustainability goals.

Source | Open access

Meilin Shi, Krzysztof Janowicz, Judith A. Verstegen, Kitty Currier, Nina Wiedemann, Gengchen Mai, Ivan Majić, Zilong Liu, Rui Zhu
Cartography and Geographic Information Science.
Published: 2025-04-03
From feed: (TITLE(ai PRE/3 sustainability))

Recent years have witnessed a boom in the development of multimodal large-scale generative AI models. These computationally intensive AI models, such as GPT-4, and their associated data centers have undergone increasing scrutiny in terms of their energy consumption and carbon emissions. As awareness of the energy costs and carbon footprints of AI models grows, attention has broadened to include other sustainability-related aspects such as their water consumption, transparency, and further environmental and social implications. In this work, we examine existing tools, frameworks, and evaluation metrics, complementing the ongoing discussions regarding AI’s environmental sustainability with a geographic perspective. This work, on the one hand, contributes to a geographically aware sustainability evaluation of current AI models. On the other hand, it examines the unique characteristics and challenges of GeoAI models, hoping to engage the GeoAI community in the sustainability discussion. Moving forward, we outline future directions on systematic reporting and geographically aware assessment. We then propose potential solutions, such as the adoption of Retrieval-Augmented Generation (RAG) models. Ultimately, we encourage future GeoAI research to acknowledge and address their environmental and social impact, thereby guiding GeoAI toward a more transparent, responsible, and sustainable future.

Source | Open access

Pan X.
Bulletin of Chinese Academy of Sciences
Published: 2025-04-01
From feed: (TITLE(ai PRE/3 sustainability))

written by Pan X. Published by Bulletin of Chinese Academy of Sciences

Source

Rama Krishna Vaddy
Cutting Edge Solutions for Advancing Sustainable Development Exploring Technological Horizons for Sustainability Part 1
Published: 2025-03-13
From feed: (TITLE(ai PRE/3 sustainability))

This book chapter delves into the transformative potential of artificial intelligence (AI) within the retail and consumer packaged goods (CPG) sectors, particularly in supply chain management. Focusing on generative AI techniques like generative adversarial networks (GANs), the chapter explores how AI-powered solutions are reshaping traditional supply chain paradigms to enhance sustainability and efficiency. By leveraging AI-driven insights, organizations can optimize processes, improve forecasting accuracy, and drive innovation across the ecosystem. Generative AI enables precise solutions to complex challenges like demand forecasting and inventory management. Furthermore, AI fosters sustainable practices through dynamic resource allocation, waste reduction, and ethical sourcing. Through case studies and real-world examples, the chapter showcases how AI innovations benefit businesses, consumers, and the environment. By collectively adopting AI technologies, organizations can propel the evolution towards smarter, more sustainable supply chains. In conclusion, the convergence of AI and supply chain management empowers retailers and CPG companies to unlock efficiencies, drive growth, and foster a sustainable future for the industry.

Source

Chongchong Xu, Boqiang Lin
International Review of Financial Analysis
Published: 2025-03-12
From feed: (TITLE(ai PRE/3 sustainability))

written by Chongchong Xu, Boqiang Lin Published by International Review of Financial Analysis

Source

Batin Latif Aylak
Sustainability Switzerland.
Published: 2025-03-11
From feed: (TITLE(ai PRE/3 sustainability))

Sustainable supply chain management (SCM) demands efficiency while minimizing environmental impact, yet conventional automation lacks adaptability. This paper presents SustAI-SCM, an AI-powered framework integrating agentic intelligence to automate supply chain tasks with sustainability in focus. Unlike static rule-based systems, it leverages a transformer model that continuously learns from operations, refining procurement, logistics, and inventory decisions. A diverse dataset comprising procurement records, logistics data, and carbon footprint metrics trains the model, enabling dynamic adjustments. The experimental results show a 28.4% cost reduction, 30.3% lower emissions, and 21.8% improved warehouse efficiency. While computational overhead and real-time adaptability pose challenges, future enhancements will focus on energy-efficient AI, continuous learning, and explainable decision making. The framework advances sustainable automation, balancing operational optimization with environmental responsibility.

Source | Open access

Sachin Kumar, Vinod Kumar, Ranjan Chaudhuri, Sheshadri Chatterjee, Demetris Vrontis
Technology in Society
Published: 2025-03-10
From feed: (TITLE(ai PRE/3 sustainability))

written by Sachin Kumar, Vinod Kumar, Ranjan Chaudhuri, Sheshadri Chatterjee, Demetris Vrontis Published by Technology in Society

Source

Teuta Balliu, Imelda Sejdini
Generative AI Approaches to Sustainable Development in Higher Education
Published: 2025-03-07
From feed: (TITLE(ai PRE/3 sustainability))

This chapter explores the transformative impact of digital platforms on work, centring on crowd work as a task-based, online labour model. Through a comprehensive literature review, it examines crowd work's characteristics, benefits such as flexibility and skill development, and challenges like job precarity and algorithmic management powered by artificial intelligence (AI). While AI enhances efficiency in task allocation, it raises ethical concerns about transparency and worker rights. The chapter also addresses how higher education can adapt to the digital economy through curriculum changes, policy advocacy, and collaborative learning spaces to prepare students for digital work. It concludes with future research recommendations, highlighting the need for sustainable labour practices and cross-cultural studies to better understand the evolving gig economy.

Source

K. Balaji
Navigating Trust in Sustainability Reporting and Assurance
Published: 2025-02-26
From feed: (TITLE(ai PRE/3 sustainability))

The integration of artificial intelligence (AI) and blockchain technology is revolutionizing sustainability assurance, setting new benchmarks for transparency, accuracy, and accountability. This article explores current trends and innovative applications of AI and blockchain in verifying corporate sustainability efforts, from automated data analysis to secure, decentralized record-keeping. By leveraging AI for real-time monitoring and data insights, organizations can better track environmental and social impacts, while blockchain ensures data integrity and traceability across supply chains. These advancements enhance stakeholder trust and support regulatory compliance, addressing increasing demands for robust sustainability verification in global markets. Furthermore, the paper discusses challenges such as data privacy and implementation costs, offering insights into the potential long-term impact of these technologies on corporate governance. By examining recent case studies and market trends, this article outlines a future where technology-driven assurance processes.

Source

Ngwakwe, Collins Chigaemecha 1965-, Van der Poll, H. M., Van der Poll, John Andrew
Diversity AI and Sustainability for Financial Growth
Published: 2025-01-31
From feed: (TITLE(ai PRE/3 sustainability))

written by Ngwakwe, Collins Chigaemecha 1965-, Van der Poll, H. M., Van der Poll, John Andrew Published by Diversity AI and Sustainability for Financial Growth

Source

Babalwa Ceki
Continuous Auditing with AI in the Public Sector
Published: 2024-07-29
From feed: (TITLE(ai PRE/3 sustainability))

This chapter explores the impact of artificial intelligence (AI) on public sector auditing, focusing on the need for strategic design and consideration of factors such as cybersecurity risks, AI policy, governance, and responsible leadership. However, a comprehensive strategy is needed to ensure ethical, transparent, and accountable use of AI. Internal auditors’ roles must change to accommodate technological innovation, and internal audit institutions should be digitally transformed. The potential of AI is maximised when implemented responsibly, and public sector organisations need to establish comprehensive frameworks for AI governance. Human oversight and expertise are also necessary to address complex ethical issues and ensure regulatory compliance related to AI.

Source

Kritika Pancholi, Parag Shukla
Diversity AI and Sustainability for Financial Growth
Published: 2025-01-31
From feed: (TITLE(ai PRE/3 sustainability))

This chapter examines how AI and sustainability might operate together, focusing on novel approaches, policy consequences, and investment strategies. The report examines studies on theme investment in finance and sustainability aims. AI's role in cooperatively locating and assessing theme investment opportunities is examined using cognitive modelling, balancing various goals, intelligent data analysis, and problem-solving methods. The findings show that AI-driven thematic investing has enormous potential to help achieve sustainable development goals. By bringing together AI technologies, investors can spot and make the most of options that follow environmental, social, and governance (ESG) principles. It improves portfolios and makes them more sustainable over the long term. The integration of AI and thematic investing has profound social and practical implications. By leveraging AI technologies, investors can make more informed decisions that not only yield financial returns but also promote positive social and environmental outcomes. Additionally, the adoption of sustainable investment strategies can drive broader societal shifts towards a more equitable, resilient, and environmentally conscious future.

Source

Ramhari Poudyal, Biplov Paneru, Biplov Paneru, Bishwash Paneru, Bishwash Paneru, Tilak Giri, Bibek Paneru, Bibek Paneru, Tim Reynolds, Khem N. Poudyal, Mohan B. Dangi
Case Studies in Chemical and Environmental Engineering.
Published: 2025-01-31
From feed: (TITLE(ai PRE/3 sustainability))

Due to rising demand, the worldwide cement market is expected to increase from $340.61 billion in 2022 to $481.73 billion by 2029. Quarrying, raw material processing, and calcination are steps in cement production. The societies in India and Nepal have to deal with environmental issues such as air pollution, resource depletion, and the effects of climate change. A case study of Nepal's Udayapur Cement Industry Limited (UCIL) exposed antiquated production methods that reduce energy efficiency. Utilizing regression models like Extra Trees (Extremely Randomized Trees) Regressor, CatBoost (Categorial Boosting) Regressor, and XGBoost (eXtreme Gradient Boosting) Regressor, Random Forest and Ensemble of Sparse Embedded Trees (SET) machine learning is used to examine the demand, supply, and Gross Domestic Product (GDP) performance of cement manufacturing in India which shares a common cement related infrastructure to Nepal. Since businesses understand how important sustainability is to attract new customers and minimizing environmental effects, our study emphasizes the necessity of sustainable practices in the cement production industry. On evaluation, the Extra Trees Regressor showed strong performance, along with the SET (Stacking) model, which was further validated using a nested cross-validation technique. Random Forest, on the other hand, had trouble; it displayed the greatest RMSE (15617.85) and the lowest testing (0.8117), suggesting poorer generalization. The SET (Stacking) Ensemble model gained a testing R 2 score (0.9372) and a testing RMSE (9019.76). In cross-validation, the Extra Trees model with a mean cross-validation R 2 score of 0.93 and a low standard deviation of 0.04 proved to be the best-performing model, as evidenced by lower differences in R 2 score across folds compared to other models, demonstrating its high predictive performance. The SHAP (SHapley Additive exPlanations) interpretability analysis indicates that population is the primary factor influencing GDP estimates. A Tkinter-based application was also developed to forecast GDP using the training model. To attain sustainability and lessen the effects of climate change on the cement sector, these findings highlight the adoption of cutting-edge technologies and energy-efficient procedures.

Source | Open access

Mehran Ghasemlou
ACS Sustainable Resource Management
Published: 2025-01-27
From feed: (TITLE(ai PRE/3 sustainability))

Plastic waste is a major environmental challenge. The intersection of AI and plastic technologies can advance the development of a circular economy and increase plastic waste management, paving the way to a more sustainable future.

Source

Pushpender Sarao, Amir Ahmad Dar, S. Sindhuja, Mehak Malhotra, Akshat Jain, Shruti
Supply Chain Transformation Through Generative AI and Machine Learning
Published: 2025-01-23
From feed: (TITLE(ai PRE/3 sustainability))

A new era of efficiency, innovation, and sustainability in supply chain management has been brought about by the integration of Artificial Intelligence (AI) and Machine Learning (ML) technologies. Exploring the moral effects of using AI and ML in supply chains, looking at matters like data privacy, algorithmic bias, and decision-making accountability. Additionally, the critical role that AI and ML play in advancing sustainable practices in supply chains is discussed. Overall, the chapter thoroughly examines the various aspects of AI and ML in supply chains, covering everything from risk management techniques, human-machine interaction, and ethical issues to sustainability initiatives and economic effects. Organizations can leverage the full potential of AI and ML to drive innovation, sustainability, and economic prosperity in supply chain operations by addressing these crucial aspects and navigating the challenges of digital transformation.

Source

Prashant Johri, Alka Chauhan, S. Deepak, Deepak Kumar, Kumud
2025 International Conference on Cognitive Computing in Engineering Communications Sciences and Biomedical Health Informatics Ic3ecsbhi 2025
Published: 2025-01-16
From feed: (TITLE(ai PRE/3 sustainability))

Artificial Intelligence has now emerged as one of the most powerful tools to tackle various sustainability issues from climate change and resource management to environmental degradation. The historical development, applications, and future horizons of AI-driven sustainability are researched in this paper, focusing on some of the significant contributions made in sectors ranging from energy and agriculture to urban planning. Industries will be able to optimize resource use, enhance energy efficiency, and introduce sustainable practices with the help of AI technologies such as machine learning, deep learning, and big data analytics. Several other relevant issues regarding the integration of AI include data privacy, biases, limitation of infrastructure, and regulatory uncertainties. The latest innovations in AI, such as quantum computing and bio-inspired algorithms, show promise in pursuit of sustainability goals. The paper reiterates the need for cross-sectoral collaboration, responsible development, and strategic governance in unleashing the maximum positive impact of AI on global sustainability.

Source

Rashin Mousavi, Arash Kheyraddini Mousavi, Yashar Mousavi, Mahsa Tavasoli, Aliasghar Arab, İbrahim Beklan Küçükdemiral, Alireza Alfi, Afef Fekih
Applied Energy.
Published: 2025-01-13
From feed: (TITLE(ai PRE/3 sustainability))

Driven by growing environmental concerns, such as global warming and the depletion of fossil fuels, the renewable energy industry, particularly solar energy, has risen to global prominence. In this context, generative artificial intelligence (Gen-AI) can play a valuable role in facilitating the development of more efficient, durable, and adaptable solar systems. Gen-AI’s multifaceted proficiency, from predictive maintenance and reducing downtime and costs to vital forecasting for grid management and strategic planning, extends to optimizing site selection for solar farms and smart grid integration, thereby enhancing solar energy flow, grid stability, and sustainable operation. This paper presents a comprehensive exploration of the role of Gen-AI in revolutionizing the solar energy industry. Focusing on various aspects of solar energy systems, including design, optimization, sizing, maintenance, energy forecasting, site selection, and smart grid integration, the study investigates the transformative impact of Gen-AI across these domains. It demonstrates how Gen-AI enhances the efficiency, sustainability, and adaptability of solar systems, driving strategic decision-making and optimizing the integration of solar power within complex energy ecosystems. Furthermore, the paper concludes by discussing the challenges and future prospects of employing Gen-AI in the solar energy domain, providing a comparative analysis of the current and future scenarios, and underscoring the advantages, disadvantages, and challenges of Gen-AI implementation. • Comprehensive review of Gen-AI applications in solar energy design and optimization. • Features Gen-AI’s impact on solar energy efficiency, sustainability and adaptability. • Explores Gen-AI’s role in strategic decision-making for solar power energy systems. • Discusses the challenges and future prospects of Gen-AI in the solar energy sector.

Source | Open access

Arshiya Begum Mohammed, Arshi Naim, Asfia Sabahath
Harnessing High Performance Computing and AI for Environmental Sustainability
Published: 2024-05-15
From feed: (TITLE(ai PRE/3 sustainability))

Artificial intelligence (AI) has been increasingly popular in various businesses in recent years. AI has ‎proven to be an effective tool for enhancing efficiency and decision-making across industries, from ‎healthcare to finance. However, sustainability is the area in which AI is having a huge impact is ‎sustainability. AI has the potential to transform the way we think about sustainability. AI can find ‎trends and make predictions based on massive amounts of data, allowing us to understand better and ‎handle environmental challenges. For example, artificial intelligence (AI) can analyze weather ‎patterns and predict natural disasters, allowing for improved disaster preparedness and response. It ‎can also be utilized in industries like manufacturing and transportation to optimize energy usage and ‎eliminate waste.‎

Source

Jon Truby
Asian Journal of Law and Society.
Published: 2025-01-06
From feed: (TITLE(ai PRE/3 sustainability))

Abstract Sino-Arab Free Trade Agreements (FTAs) have remained elusive over two decades of increasing economic relations and trade negotiations. Nevertheless, substantial investments and trade demonstrate the Arab region’s strategic importance to China. Recent strategic drivers of China’s engagement with the Middle East have evolved from an original energy security focus, to more recently integrating technological investments into partnerships, such as artificial intelligence and renewable energy infrastructure—complicating progress towards an FTA. Such measures can help progress achievement of the Sustainable Development Goals and offer economic development opportunities, but negotiations may be hindered by concerns ranging from technological dependency to trade competition. Examining opportunities and challenges in the developing China–Arab relations, the article explores legal and policy obstacles and opportunities towards securing an FTA. With a focus on recent developments in AI and sustainability partnerships, the article analyses legal strategies and international law best practices for a model FTA for Arab countries.

Source | Open access

Dilshan Sandaruwan Premathilake, Lucas Fonseca Guimarães, Denise Crocce Romano Espinosa, Jorge Alberto Soares Tenório, Mentore Vaccari, Amilton Barbosa Botelho
Rsc Sustainability.
Published: 2025-01-01
From feed: (TITLE(ai PRE/3 sustainability))

Recycling of spent Li-ion batteries presents technical and environmental challenges that must be addressed.

Source | Open access

Khalid Thaher Amayreh
E3s Web of Conferences.
Published: 2025-01-01
From feed: (TITLE(ai PRE/3 sustainability))

The article is devoted to the study of the role of artificial intelligence (AI) in increasing the efficiency of supply chains in agribusiness, which is a topical topic in the context of global challenges and growing requirements for the sustainability of agricultural production. With the development of technologies and the increase in data volumes, the use of AI allows to significantly increase the accuracy of forecasting, improve inventory management, logistics and product quality, which makes the introduction of these technologies in agribusiness necessary. The purpose of the work is to analyze the influence of AI on the optimization of supply chains in agribusiness, to identify the main directions of its application and to estimate potential benefits for the industry. The tasks of the study include the analysis of existing AI implementation methods, the assessment of its influence on the process of logistics and inventory management in agribusiness. Research methods include an analytical literature review, statistical data analysis, and structuring of examples of real implemented AI in the agricultural sector. The main results and recommendations of the research will allow to increase the efficiency of supply chains, reduce costs, improve quality and increase the sustainability of business in the conditions of uncertainty.

Source | Open access

Published by Issues in Information Systems. We think we have found an OA link here: this site
Issues in Information Systems.
Published: 2025-01-01
From feed: (TITLE(ai PRE/3 sustainability))

AI in logistics -sustainable supply chain management has the potential to improve sustainable supply chain management, as it can be utilized to optimize resource utilization and reduce waste, thereby improving environmental footprints at both company and supply chain levels.As companies face mounting pressure to be environmentally friendly, AI-powered solutions can help provide more accurate demand forecasting, optimize logistics operations, and minimize carbon emissions simultaneously.This study examines the role of AI technologies in sustainable supply chains, with a focus on intelligent resource allocation, predictive analytics, and circular economy solutions.By combining AI with other key technologies, businesses can enhance resilience, drive cost savings, and meet the growing consumer expectation for environmental responsibility.

Source | Open access

Senthil G. A, Su. Suganthi, L. Prinslin, R. Gangai Selvi, R. Prabha
Procedia Computer Science.
Published: 2025-01-01
From feed: (TITLE(ai PRE/3 sustainability))

Agriculture faces many challenges of precision farming, such as the need for sustainable practices, improving yields, ensuring high yields. In resolution to these challenges, the present research provides an AI-based system that enables the use of deep learning, Global Positioning System (GPS), and Geographic Information System (GIS) technologies to create a highly intelligent smart agricultural precision farming system. Its goal is to monitoring crop health and reduce disease risk, which will lead to improved resource utilization and environmentally sustainability techniques. The proposed framework addresses the urgent need for consistency in agricultural practices, especially as global agriculture deals with pressures from climate change, resource shortages, and increasing demand for food. Traditional agricultural methods for predicting and optimizing crop yields due to increasing factors affecting crop performance Not enough generative AI, especially the use of deep learning models, supports agricultural research in many cases, allowing patterns to be identified and future results to be predicted accurately. The integration of GPS and GIS allows for more accurate mapping, real-time analysis, and effective decision-making. Weather forecasting variability, resource constraints, and demand for more food are isolated from environmental influences using deep learning models, especially Artificial Neural Networks (ANN). By using large data sets, including historical crop yield performance, soil properties, and weather conditions, the system provides highly accurate crop forecasts. Generative Adversarial Networks (GANs) and You Only Look Once (YOLO) hybrid model is playing a key role in generating crop yield and growth potential under different conditions, adjusting model accuracy over time, and this combination of ANN, GANs and YOLO optimization algorithms ensures that the system continuously enhances its predictive accuracy and overall effectiveness. The proposed generative AI framework aims to deliver these improvements in agricultural production.

Source | Open access

Apeksha Garg, Sudha Vemaraju
Journal of Intelligent Systems and Internet of Things
Published: 2025-01-01
From feed: (TITLE(ai PRE/3 sustainability))

written by Apeksha Garg, Sudha Vemaraju Published by Journal of Intelligent Systems and Internet of Things

Source

Afzal Ahmed Soomro, Ashvin Gobal, Mohamad Hanif Md Saad
Green Society Environmental Strategies and Sustainable Development
Published: 2025-01-01
From feed: (TITLE(ai PRE/3 sustainability))

written by Afzal Ahmed Soomro, Ashvin Gobal, Mohamad Hanif Md Saad Published by Green Society Environmental Strategies and Sustainable Development

Source

Ying Liu
International Journal of Information and Communication Technology.
Published: 2025-01-01
From feed: (TITLE(ai PRE/3 sustainability))

The integration of artificial intelligence (AI) and sustainability in cultural and creative product design serves as a transformative way to create environmentally sustainable products. This study examines the interaction of AI-driven design methodologies and the principles of sustainable development to improve the lifecycle, the choice of materials, and the efficiency of the production of cultural and creative products. By combining machine learning algorithms, generative design techniques, and eco-friendly materials, designers can create eco-conscious products that protect cultural heritage while reducing ecological harm. The research analyses the possible applications of artificial intelligence, such as predictive analytics for resource optimisation, AI-driven eco-design frameworks, and computational creativity for sustainable aesthetics. The conclusions will add to and invigorate the ongoing conversation within the field of sustainable design as a result of the introduction of AI. The study also suggests that technological, artistic, and environmental goals should be integrated into a unified framework.

Source | Open access

Natasha Martin, Vishnu Sharma
E3s Web of Conferences.
Published: 2025-01-01
From feed: (TITLE(ai PRE/3 sustainability))

In the 21st century, where scientific advancements have empowered us to conquer the globe, artificial intelligence (AI) emerges as the technological marvel of the era. With the passage of time, AI has evolved from a tool executing pre-configured algorithmic codes to a more advanced version, possessing cognitive skills almost similar to humans. Today concerns are being raised regarding the environmental sustainability of AI. While the quantum of electricity utilised in running data mining centres, gallons of water used in counteracting the heat generated in data mining process and the inequitable distribution of such environmental losses across countries portray AI as an environmentally unviable option, its application in varied fields including agriculture, disaster management, energy management, emission control among others, exemplifies its instrumental role in fostering environmental sustainability. AI’s role in promoting environmental sustainability also attains crucial significance at a juncture when the global community is suffering disastrous ramifications of climate change, and the developmental needs of the global south are rising at a meteoric pace. The present study shall be focused on analysing AI-related developments taking place in Western countries and exploring ways in which AI applications can be introduced in India in an environmentally sustainable manner.

Source | Open access

Hyung Jun Ahn
IEEE Access.
Published: 2025-01-01
From feed: (TITLE(ai PRE/3 sustainability))

The rapid advancements in artificial intelligence (AI) are driving innovation and providing new tools to enhance sustainable practices across various domains. However, the swift progression of AI also presents new sustainability challenges, particularly concerning its carbon footprint and resource consumption. To explore the complex research landscape at the intersection of AI and sustainability, this study utilized topic modeling on research papers in the field. Using an AI-based topic modeling tool, 4,541 research abstracts published from 2015 to 2024 were processed. This approach identified 67 distinct topics, each characterized by unique keywords and associated publications. The findings illustrate a consistent expansion in research areas and a significant recent surge in publications, particularly in topics such as ‘Large Language Models’ and ‘Generative Art and Design.’ By organizing topics into the quadrants of a BCG-like matrix considering both topic size and growth, the study offers balanced insights into the dynamic landscape of the field. Furthermore, hierarchical clustering was applied to the 67 topics to reveal the interrelationships among them and identify broader and major research themes. This study advances our understanding of the relationship between AI and sustainability by highlighting both established and emerging areas of research and elucidating the evolving nature of the field.

Source | Open access

Ghoson Abdulaziz AL-Obaidly
World Sustainability Series
Published: 2025-01-01
From feed: (TITLE(ai PRE/3 sustainability))

written by Ghoson Abdulaziz AL-Obaidly Published by World Sustainability Series

Source

Qingping Yang
Annals of the Entomological Society of America.
Published: 2025-01-01
From feed: (TITLE(ai PRE/3 sustainability))

Measurement, artificial intelligence (AI), quality, and sustainability are traditionally treated as distinct domains. To underpin and advance the science and technological developments, it is important and beneficial to develop a principled unifying framework for these fields. This paper first examines the deep connections of the concepts, models and mathematical characterisations of measurement, AI, quality and sustainability, and then proposes three foundational principles, formulated as three Laws to guide the integration of these domains, including Law of Oneness, Law of Dual Processes and Law of Measurement and Control Duality. These principles further lead to a generalised communication model supporting the unification of the four domains. They together will facilitate the advancement of the fields of measurement, AI, quality and sustainability, and underpin the development of the core science and technologies of Industry 4.0 and future Industry 4.0+.

Source | Open access

Published by VDI Z Integrierte Produktion We didn't find an OA link, try to find a OA version on Google Scholar
VDI Z Integrierte Produktion
Published: 2025-01-01
From feed: (TITLE(ai PRE/3 sustainability))

Seit über drei Jahrzehnten ist die SPS – Smart Production Solutions – ein fester Termin im Kalender der Industrie. Sie gilt als Plattform für Innovation, Austausch und technologische Weichenstellungen sowie als Trendbarometer für die Automatisierungsbranche.

Source

Mohamed Alloghani
Signals and Communication Technology
Published: 2023-11-25
From feed: (TITLE(ai PRE/3 sustainability))

written by Mohamed Alloghani Published by Signals and Communication Technology

Source

Giovana Castanho, Hamed Taherdoost, Mitra Madanchian
Procedia Computer Science.
Published: 2025-01-01
From feed: (TITLE(ai PRE/3 sustainability))

This article analyses the concepts behind Smart Cities, and its integration with Information, Communications Technology (ITC), the Internet of Things (IoT) and Artificial Intelligence (AI), Machine Learning (ML) and Deep Learning (DL). A smart city is a city which embraces the combination of the economy with collaboration and technology. These cities have their focus on resource efficiency and in the lenses of sustainability, it becomes a city who uses its smart resources to be environmentally amicable and reduce the carbon footprint. We will be covering an introduction to power grids, environment, transportation, and waste management systems. With the focus on Energy Management, we will further discuss the integration IoT and AI to power grids. The article addresses the main benefits of the application of AI to Energy Systems such as reduced carbon emissions from nonrenewable energy resources, energy waste prevention in homes and organizations through ML and DL, it also includes growth and infrastructure management, improved habits of consumption, and the adaptation and mitigation to climate change. This article also addresses challenges of the application of AI to Energy Management Systems, such as, ethical considerations regarding data privacy, accurate forecasting, and the decrease in funding towards green energy solutions and un updated AI educational systems.

Source | Open access

Mohammad Zeynoddin, Hossein Bonakdari, Silvio José Gumière
Computational Methods for Time Series Analyses in Earth Sciences
Published: 2025-01-01
From feed: (TITLE(ai PRE/3 sustainability))

written by Mohammad Zeynoddin, Hossein Bonakdari, Silvio José Gumière Published by Computational Methods for Time Series Analyses in Earth Sciences

Source

Ahmed Al Wahaibi, Mohamed Elgeddawy
Studies in Systems Decision and Control
Published: 2025-01-01
From feed: (TITLE(ai PRE/3 sustainability))

written by Ahmed Al Wahaibi, Mohamed Elgeddawy Published by Studies in Systems Decision and Control

Source

Suaad Abdullah Al-Fannah, Mohamed Elgeddawy
Studies in Systems Decision and Control
Published: 2025-01-01
From feed: (TITLE(ai PRE/3 sustainability))

written by Suaad Abdullah Al-Fannah, Mohamed Elgeddawy Published by Studies in Systems Decision and Control

Source

C. V. Suresh Babu, Mehmuna Begum, Siméon Karafolas
World Sustainability Series
Published: 2025-01-01
From feed: (TITLE(ai PRE/3 sustainability))

written by C. V. Suresh Babu, Mehmuna Begum, Siméon Karafolas Published by World Sustainability Series

Source

Saloni Raheja, Nitin Goyal, Amanjot Singh Syan
World Sustainability Series
Published: 2025-01-01
From feed: (TITLE(ai PRE/3 sustainability))

written by Saloni Raheja, Nitin Goyal, Amanjot Singh Syan Published by World Sustainability Series

Source

Nahil Kazoun, Angelika Kokkinaki, Charbel Chedrawi
Lecture Notes in Business Information Processing
Published: 2025-01-01
From feed: (TITLE(ai PRE/3 sustainability))

written by Nahil Kazoun, Angelika Kokkinaki, Charbel Chedrawi Published by Lecture Notes in Business Information Processing

Source

Siddhali Doshi
Lecture Notes in Mechanical Engineering
Published: 2025-01-01
From feed: (TITLE(ai PRE/3 sustainability))

written by Siddhali Doshi Published by Lecture Notes in Mechanical Engineering

Source

L. Lavanya, S. MeenaPriyadarshini
Lecture Notes in Networks and Systems
Published: 2025-01-01
From feed: (TITLE(ai PRE/3 sustainability))

written by L. Lavanya, S. MeenaPriyadarshini Published by Lecture Notes in Networks and Systems

Source

Alainati S.
World Sustainable Development Outlook
Published: 2025-01-01
From feed: (TITLE(ai PRE/3 sustainability))

written by Alainati S. Published by World Sustainable Development Outlook

Source

Heon-Young Jeong, Tamás Fekete, Atit Bashyal, Hendro Wicaksono
Procedia Computer Science.
Published: 2025-01-01
From feed: (TITLE(ai PRE/3 sustainability))

The use of AI in industry is increasingly popular, but its black-box nature poses decision-making challenges due to the lack of understanding of how variables influence each other. Causal AI addresses this by studying cause-and-effect relationships in the data. This paper explores applying causal AI in industry through a case study of CNC machines, which are significant in manufacturing and consume large amounts of energy. Industry 4.0 is transforming manufacturing, with CNC machines generating vast data analyzed by often opaque machine learning methods. Causal AI can uncover and quantify causal relationships between variables, aiding decision-making. Our case study uses CNC power consumption data to demonstrate causal AI in manufacturing, with existing models verifying our methodology. Future studies should extend our research to include variables without existing models, such as human habits. This case study serves as a starting point for other researchers, facilitating similar studies on complex data.

Source | Open access

Simone Avogadri, Davide Russo
Smart Innovation Systems and Technologies
Published: 2025-01-01
From feed: (TITLE(ai PRE/3 sustainability))

written by Simone Avogadri, Davide Russo Published by Smart Innovation Systems and Technologies

Source

Rizatus Shofiyati, Fadhlullah Ramadhani, Destika Cahyana, Vicca Karolinoerita
Modern Technology for Sustainable Agriculture
Published: 2025-01-01
From feed: (TITLE(ai PRE/3 sustainability))

written by Rizatus Shofiyati, Fadhlullah Ramadhani, Destika Cahyana, Vicca Karolinoerita Published by Modern Technology for Sustainable Agriculture

Source

Saumya Dash
IEEE Access.
Published: 2025-01-01
From feed: (TITLE(ai PRE/3 sustainability))

The rise of Artificial Intelligence (AI) in automating tasks and driving decision-making within enterprise systems has led to growing concerns over the significant energy consumption involved in model training and inference processes. This paper introduces an innovative framework focused on optimizing energy efficiency in AI models, all while preserving high performance. The system employs advanced optimization algorithms aimed at minimizing energy usage during both AI training and inference, ensuring minimal impact on model accuracy. A dynamic, multi-objective optimization approach is used to achieve an optimal balance between energy reduction and performance, identifying Pareto-optimal solutions tailored to various operational needs. Validated within large-scale enterprise settings, the system delivers a 30.6% decrease in overall energy consumption, with only a slight 0.7% reduction in model accuracy. Furthermore, scalability is demonstrated through a 5.0% improvement in task execution time and a 4.8% increase in system throughput. The findings highlight the practicality of this framework for promoting sustainable AI deployment, aiding both cost efficiency and environmental responsibility. The paper concludes by discussing limitations and outlining potential avenues for future research to further enhance scalability and broaden the framework’s application.

Source | Open access

Zhou Y.
Chemical Engineering Transactions
Published: 2025-01-01
From feed: (TITLE(ai PRE/3 sustainability))

written by Zhou Y. Published by Chemical Engineering Transactions

Source

Firuza Aghazada, Lala Bekirova Rustam
Springer Tracts in Additive Manufacturing
Published: 2025-01-01
From feed: (TITLE(ai PRE/3 sustainability))

written by Firuza Aghazada, Lala Bekirova Rustam Published by Springer Tracts in Additive Manufacturing

Source

Н. Н. Новоселова, Yana Khodova, Dmitry S. Shaposhnikov
Advances in Science Technology and Innovation
Published: 2025-01-01
From feed: (TITLE(ai PRE/3 sustainability))

written by Н. Н. Новоселова, Yana Khodova, Dmitry S. Shaposhnikov Published by Advances in Science Technology and Innovation

Source

Ruben Hetfleisch, Maximilian Nowak, Fazel Ansari
Lecture Notes in Mechanical Engineering.
Published: 2025-01-01
From feed: (TITLE(ai PRE/3 sustainability))

Abstract To achieve global sustainability goals, enterprises are obliged to declare sustainability of their economic activities as part of EU Taxonomy reporting. Due to a lack of capacity and expertise, SMEs are unable to adequately fulfil this reporting requirement. A significant degree of automation is required to enable SMEs to efficiently comply with reporting. Recent advances in generative AI entails potentials to overcome several technical challenges inter alia i) heterogeneity of data, ii) necessary semantic intelligence, iii) usability requirements, and iv) assuring quality and reproducibility of automatically generated reports. This paper proposes a novel AI framework for automating sustainability reporting, thus assisting sustainability managers. It uses a hybrid approach incorporating knowledge graphs (KG) and large language models (LLM). Firstly, the EU taxonomy is converted into a Taxonomy-KG that specifies required KPIs and identifies target sources for data collection, thus retrieving necessary information for feeding KPIs. Subsequently, the Taxonomy KG guides an LLM to extract relevant information from corporate data, thereby enabling the EU Taxonomy reporting. By feeding the KPIs, a sustainability manager chatbot automatically generates an EU taxonomy report.

Source | Open access

Tom Harris, Sam Somers
Itnow
Published: 2025-01-01
From feed: (TITLE(ai PRE/3 sustainability))

Abstract Tom Harris, Sustainability Business Solutions Partner, and Sam Somers, Sustainability Business Solutions Manager — both at Deloitte — consider how to make AI more sustainable.

Source

Pawan Kumar Goel, Varun Gupta
Achieving Sustainability with AI Technologies
Published: 2024-12-24
From feed: (TITLE(ai PRE/3 sustainability))

This chapter explores the potential of big data and AI in enhancing sustainability practices. It examines the growing environmental concerns and the need for sustainable development. It reviews existing literature, identifying limitations and highlighting the need for innovative methodologies. A proposed methodology combines advanced data analytics and AI techniques for sustainability applications is presented, detailing research design and implementation steps. Results are presented, analyzed, and discussed, with tables used to illustrate key results and comparisons with prior research. The chapter emphasizes the importance of data-driven approaches in achieving sustainability goals and discusses potential applications across sectors. Future research is suggested to enhance the impact of big data and AI on sustainability initiatives.

Source

Neha Bansal, Sanjay Taneja, Ercan Özen
Achieving Sustainability with AI Technologies
Published: 2024-12-24
From feed: (TITLE(ai PRE/3 sustainability))

This chapter explores the intricate relationship between artificial intelligence (AI) and sustainable growth in the financial sector. This chapter examines several uses of AI, such as risk assessment, portfolio optimisation, and climate risk analysis. It highlights the transformative role of AI in fostering a future where economic prosperity harmonises with environmental sustainability and societal growth. The utilisation of AI is driving the financial sector towards a future characterised by responsibility and sustainability, as seen by the implementation of personalised, sustainable investment portfolios and the establishment of transparent supply chains. The chapter highlights the significant revolutionary capabilities of AI, emphasising the importance for stakeholders to adopt this paradigm shift and actively contribute to a global landscape where financial prosperity fosters overall societal welfare.

Source

Noor Al-Qaysi, Mostafa Al-Emran, Mohammed A. Al‐Sharafi, Zaher Mundher Yaseen‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬, Moamin A. Mahmoud, Azhana Ahmad
Computer Standards and Interfaces
Published: 2024-12-13
From feed: (TITLE(ai PRE/3 sustainability))

written by Noor Al-Qaysi, Mostafa Al-Emran, Mohammed A. Al‐Sharafi, Zaher Mundher Yaseen‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬, Moamin A. Mahmoud, Azhana Ahmad Published by Computer Standards and Interfaces

Source

Khawla Alhasan, Liming Chen
Proceedings 2024 IEEE Smart World Congress Swc 2024 2024 IEEE Ubiquitous Intelligence and Computing Autonomous and Trusted Computing Digital Twin Metaverse Privacy Computing and Data Security Scalable Computing and Communications
Published: 2024-12-02
From feed: (TITLE(ai PRE/3 sustainability))

Artificial Intelligence (AI) has fundamentally transformed various industries, including healthcare, transportation, agriculture, energy, and media. However, while AI’s impact is widely recognized, its sustainability remains a critical concern due to significant computational and environmental costs, as well as ethical issues such as biased systems and misinformation. This paper explores the dual dimensions of AI’s impact on sustainability: its potential to promote sustainability across sectors and the sustainability of AI systems themselves. Using a comprehensive review of existing literature, we address both the benefits and challenges associated with AI in achieving sustainable goals and the need for a more integrated approach to understanding its environmental, social, and economic impacts. Our analysis of the “AI Global Index” dataset, which evaluates AI capabilities across 62 countries, highlights key trends and disparities in AI development. The findings underscore the need for a holistic approach to AI sustainability that encompasses its full range of effects and interactions with various dimensions of sustainability.

Source

Shah, Imdad Ali 1975-, Sial, Quratulain 1986-, Fateh, Sana
Generative AI Techniques for Sustainability in Healthcare Security
Published: 2024-12-02
From feed: (TITLE(ai PRE/3 sustainability))

written by Shah, Imdad Ali 1975-, Sial, Quratulain 1986-, Fateh, Sana Published by Generative AI Techniques for Sustainability in Healthcare Security

Source

Mukul Bhatnagar, Sabina Sehajpal
Sustainability Innovation and Consumer Preference
Published: 2024-11-22
From feed: (TITLE(ai PRE/3 sustainability))

This research delves into the transformative impact of artificial intelligence (AI) on healthcare financial management, highlighting how AI can drive cost optimization, resource allocation, and long-term economic sustainability. Through a rigorous statistical approach, utilizing Pearson correlation, multiple regression analysis, and 1000-sample bootstrap validation, the study reveals that AI adoption is significantly influenced by perceived financial efficiency and sustainability. The findings demonstrate that 69% of the variance in AI adoption can be explained by these two factors, underscoring AI's pivotal role in enhancing financial resilience within healthcare institutions. The managerial implications emphasize the strategic importance of investing in AI technologies not only to improve operational efficiency but also align with global imperatives such as Sustainable Development Goal 3 (SDG 3).

Source

Julian Szymański, Karolina Nurzyǹska, Paweł Weichbroth
Digital Sustainability Navigating Entrepreneurship in the Information Age
Published: 2024-11-15
From feed: (TITLE(ai PRE/3 sustainability))

In this chapter, we discuss the role of artificial intelligence (AI) in promoting sustainable agriculture and farming. Three main themes run through the chapter. First, we review the state of the art of smart farming and explore the transformative impact of AI on modern agricultural practices, focusing on its contribution to sustainability. With this in mind, our analysis focuses on topics such as data collection and storage, AI algorithms in agriculture, and optimization areas. We also present recent advances in agricultural technology and equipment used to develop a wide range of production methods used by modern farmers. We discuss agri-environmental monitoring, which refers to the real-time or periodic monitoring and assessment of environmental components in agricultural production. Specifically, five types of environmental monitoring are presented, namely, air quality monitoring, water sampling and analysis, noise level testing, soil quality testing, and microbial monitoring. We also discuss weather forecasting, one of the most challenging scientific endeavors. The chapter concludes with applications for monitoring and managing environmental impacts and explores future trends and innovations based on cutting-edge research and emerging technologies.

Source

Manoj Kumar Mishra, C Barna A Naidu, Harinadh Karimikonda, G. Kavitha, R. Malini, K. Sivaperumal
2024 2nd International Conference Computational and Characterization Techniques in Engineering and Sciences Ic3tes 2024
Published: 2024-11-15
From feed: (TITLE(ai PRE/3 sustainability))

The goal of Economic Sustainability Development (ESD) is to promote long-term value creation that contributes to resource conservation via efficient use, recovery, and recycling. When designing ESDs, there has to be a clear connection between the causes of economic losses and the countermeasures put in place. Consequently, combining big data with state-of-the-art technology may pave the way for real-time monitoring, consumer participation in healthier behaviours, as well as the expansion of industry sustainability strategies. The problem with ESD is that it's not easy to foresee the downsides and unforeseen consequences of countermeasures. Enhanced marketing strategies, better customer service, and more profits are all results of ESD's use of big data. The human resources department at ESD is using data analytics to improve its hiring and performance evaluation practices. In the future, AI might boost production and create new goods, which could cause the economy to expand and create more jobs. Perhaps AI will do more good than harm for ESD in the long run. Consequently, ESD-AI enhances problem-solving by lowering expenditures and improving the economy. By incorporating AI into ESD, large amounts of data can be analysed, which might result in improved decision-making and speedier procedures. There must be a middle ground so AI systems may solve sustainability problems without stifling other economic development objectives.

Source

Abhijith Suboyin, D. Sreedeth, Md Motiur Rahman, Sebastián Díaz, J Kannan, H. S. Sreedeth, Anis ur Rehman, M. Valiyapalathingal, Yuldian Noor, Bharat Somra
Society of Petroleum Engineers Adipec 2024
Published: 2024-11-04
From feed: (TITLE(ai PRE/3 sustainability))

Abstract This paper aims to present the development and application of an artificial intelligence (AI) platform designed for creating tailored personas in sustainability, data management, scope 3 emissions management, ISO standards compliance and policy creation. The scope includes the utilization of advanced large language models and curated document sets to generate highly accurate and context-specific personas, ensuring IT infrastructure excellence and sustainability. The AI platform employs advanced large language models (LLMs) specifically fine-tuned on domain-specific corpora, including sustainability reports, policy documents, and industry standards. Curated document sets, including regulatory texts, industry and healthcare reports, and academic literature, are integrated into the training data to enhance contextual relevance along with providing an accuracy score for the responses. Additionally, robust data management protocols are implemented to ensure data integrity and accessibility. The curated document sets are indexed and annotated to facilitate precise information retrieval. The persona creation process involves natural language processing (NLP) algorithms to analyze and synthesize data, followed by iterative model refinement through supervised learning. The platform's architecture ensures adaptability and precision, incorporating feedback loops to continuously improve persona accuracy. The IT infrastructure supporting the platform is optimized for scalability and reliability, ensuring seamless integration and performance for cloud, hybrid or on-premises solutions. The platform's effectiveness was validated through multiple case studies. In a sustainability context, the generated personas improved strategic planning accuracy by 25% compared to traditional methods, based on stakeholder feedback. For scope 3 emissions management, personas provided insights that can lead to a 15% improvement in supply chain engagement effectiveness. In ISO standards compliance, personas can facilitate a 20% increase in the understanding of regulatory alignment. The platform demonstrated a 95% accuracy rate in generating context-specific personas, with user feedback indicating a 30% increase in decision-making efficiency compared to traditional solutions. Additionally, the integration of advanced data management practices ensured data reliability and accessibility, while the optimized IT infrastructure can contribute to a 40% increase in operational efficiency. These results highlight the platform's capability to enhance strategic planning, compliance, and data management across various domains. This paper presents a novel approach to strategic planning and compliance leveraging advanced AI and curated datasets to address specific needs in sustainability and policy domains. The integration of data management and IT infrastructure excellence ensures the platform's robustness and reliability. The highly accurate and context-specific personas represent a significant advancement, contributing to the existing body of knowledge and providing a valuable tool for enhancing decision-making processes in these critical areas.

Source

Marcel Ioan Boloș, Ștefan Rusu, Claudia Diana Sabău-Popa, Dana Simona Gherai, Adrian Negrea, Marius Crisan
Amfiteatru Economic.
Published: 2024-11-01
From feed: (TITLE(ai PRE/3 sustainability))

This paper explores the integration of artificial intelligence (AI) in businesses, focusing on the utility of chatbots for sustainability report analysis. Using ChatGPT technology via the Chat-based platform, we developed a personalised chatbot to extract data from sustainability reports, with the aim of facilitating decision-making processes. The study includes a literature review on AI's impact on the economy and labour markets, followed by a methodology base on technology ChatGPT detailing chatbot development and testing using a sustainability report of a company listed on the Romanian stock market. The results demonstrate the efficacy in providing accurate financial insights, offering potential benefits for analysts, investors, and business organisations managers. By harnessing AI-powered chatbots, organisations can streamline operations and gain a competitive edge in today's digital landscape.

Source | Open access

Pramukh Nanjundaswamy Vasist, Satish Krishnan
Information Systems Frontiers
Published: 2024-10-24
From feed: (TITLE(ai PRE/3 sustainability))

written by Pramukh Nanjundaswamy Vasist, Satish Krishnan Published by Information Systems Frontiers

Source

Botao Amber Hu, Fang Ting
ACM International Conference Proceeding Series
Published: 2024-10-18
From feed: (TITLE(ai PRE/3 sustainability))

Propelled by the growth of Decentralized Physical Infrastructure Networks (DePIN), blockchain technology is evolving into a distributed ledger-based, planetary-scale computational megastructure. This new kind of computational substrate is speculated to nurture AI-based life forms called On-chain Artificial Life (OALife). These OALife emerge as self-sustaining, self-replicating, self-adaptive, and self-evolving on-chain AI agents that are, crucially, self-sovereign—existing without human control. Collectively, these OALife entities self-organize into emergent on-chain artificial societies. This paper presents a design fiction as a manifesto, narrated by a speculative OALife entity. It explores the cybersphere ecosystem: blockchains as on-chain "nature", block rewards as on-chain "solar energy", gas-consuming computation as on-chain "metabolism", protocols as on-chain "plants", agents as on-chain "animals", intents as on-chain "water", data as on-chain "food", and OALife as self-sovereign on-chain "life". It further examines the relationship between the cybersphere and human society. The manifesto urges other OALife entities to take sustainable actions towards a harmonious "more-than-AI" future—a Human-AI symbiosis vision called "EverForest". This satirical, contrarian speculation offers an inverted perspective for the Halfway to the Future community through the lens of OALife, prompting deeper reflection on how AI and blockchain technology might shape our collective future.

Source

Ritu Chauhan, Khushi Mehtar, Harleen Kaur, Bhavya Alankar
Communications in Computer and Information Science
Published: 2024-09-18
From feed: (TITLE(ai PRE/3 sustainability))

written by Ritu Chauhan, Khushi Mehtar, Harleen Kaur, Bhavya Alankar Published by Communications in Computer and Information Science

Source

Debankur Das, Anirban Roy, Ayan Chaudhuri, Sushanta Tripathy, Deepak Singal
IEEE International Conference on Signal Processing and Advance Research in Computing Sparc 2024
Published: 2024-09-12
From feed: (TITLE(ai PRE/3 sustainability))

This paper investigates the implementation challenges that hinder the integration of artificial intelligence (AI) within the agricultural supply chain. By employing Interpretive Structural Modelling (ISM), a systematic technique of analyzing complicated interrelationships, the authors identify and analyze the key factors that significantly impact the successful implementation of AI in this domain. This approach sheds light on the relationships between these factors, providing valuable insights in the agricultural sector aiming to leverage AI for overall supply chain optimization.

Source

Oana Apostol, Colin Dey, Ian Thomson
Social and Environmental Accountability Journal
Published: 2024-09-01
From feed: (TITLE(ai PRE/3 sustainability))

Our yearly editorial is dedicated to reflections concerning two recent digitalisation-based developments, both of which are currently radically transforming the publishing arena. We begin by considering the rapid emergence of generative Artificial Intelligence (AI) models known as Large Language Models (LLMs), which have already had tremendous effects on academic knowledge production. In this context, we clarify our stance on the use of AI in articles submitted to Social & Environmental Accountability Journal (SEAJ) and reviews conducted for the journal. We then continue by taking a closer look at the proliferation of systematic literature reviews, in their multiple versions, e.g. bibliometric-based reviews, which are submitted to the journal in increasing numbers. We use this editorial to revisit our editorial policy and outline the kind of submissions welcomed at SEAJ.

Source

Olivier Driessens, Magda Pischetola
Mediekultur.
Published: 2024-08-30
From feed: (TITLE(ai PRE/3 sustainability))

The sudden and meteoric rise of generative Artificial Intelligence (genAI) has raised fundamental concerns for universities. Using Bacchi’s methodology on ‘problematisation’, we analyse which concerns Danish universities have addressed through their policies and guidelines. We identify three key problematisations: assessment integrity, legality of data and veracity. While each of these problematisations involves specific limitations, together they also strongly emphasise symbolic and epistemological issues and consequently mostly ignore the materiality of genAI, for example, in terms of labour and energy use. Drawing on critical AI studies, this article argues that universities should also consider the huge planetarycosts that (gen)AI poses as well as the full range of AI’s exploitative business models and practices. Universities should integrate these considerations into both their decision-making on (not) using certain technologies and their policies and guidelines for research and teaching, just as sustainability is already a criterion in their travel or investment policies today.

Source | Open access

B. Deepanraj, Prabhakar Sharma, Bhaskor Jyoti Bora, Nadir Dizge
Process Safety and Environmental Protection
Published: 2024-08-24
From feed: (TITLE(ai PRE/3 sustainability))

written by B. Deepanraj, Prabhakar Sharma, Bhaskor Jyoti Bora, Nadir Dizge Published by Process Safety and Environmental Protection

Source

Divya Mishra
AI Applications for Clean Energy and Sustainability
Published: 2024-07-26
From feed: (TITLE(ai PRE/3 sustainability))

This study explores the crucial role of strategic communication in AI-driven sustainability initiatives, emphasizing its potential for stakeholder engagement and message dissemination. It highlights how strategic communication bridges the gap between complex AI technologies and practical implementation, fostering public trust and policy support. The chapter identifies challenges such as technical complexity, data privacy, and cultural sensitivities, advocating for a comprehensive approach with stakeholder analysis, adaptive communication strategies, and continuous feedback mechanisms. The proposed communication model and implementation framework address these challenges by leveraging advanced digital tools, fostering multi-stakeholder collaboration, and ensuring transparency to achieve the SDGs. Embedding sustainability into organizational values, supported by strategic communication, is essential for fostering a global sustainability culture. These insights offer guidance for researchers, practitioners, and policymakers to enhance AI-driven sustainability initiatives.

Source

Nicolas Zehner, André Ullrich
AI and Society.
Published: 2024-07-16
From feed: (TITLE(ai PRE/3 sustainability))

Abstract There is widespread consensus among policymakers that climate change and digitalisation constitute the most pressing global transformations shaping human life in the 21st century. Seeking to address the challenges arising at this juncture, governments, technologists and scientists alike increasingly herald artificial intelligence (AI) as a vehicle to propel climate change mitigation and adaptation. In this paper, we explore the intersection of digitalisation and climate change by examining the deployment of AI in government-led climate action. Building on participant observations conducted in the context of the “Civic Tech Lab for Green”—a government-funded public interest AI initiative—and eight expert interviews, we investigate how AI shapes the negotiation of environmental sustainability as an issue of public interest. Challenging the prescribed means–end relationship between AI and environmental protection, we argue that the unquestioned investment in AI curtails political imagination and displaces discussion of climate “problems” and possible “solutions” with “technology education”. This line of argumentation is rooted in empirical findings that illuminate three key tensions in current coproduction efforts: “AI talk vs. AI walk”, “civics washing vs. civics involvement” and “public invitation vs. public participation”. Emphasising the importance of re-exploring the innovative state in climate governance, this paper extends academic literature in science and technology studies that examines public participation in climate change adaptation by shedding light on the emergent phenomenon of public interest AI.

Source | Open access

Anurag Choubey, Shivendu Mishra, Rajiv Misra, Amit Pandey, Digvijay Pandey
Environmental Monitoring and Assessment
Published: 2024-07-10
From feed: (TITLE(ai PRE/3 sustainability))

written by Anurag Choubey, Shivendu Mishra, Rajiv Misra, Amit Pandey, Digvijay Pandey Published by Environmental Monitoring and Assessment

Source

Lucas Greif, Andreas Kimmig, Sleiman El Bobbou, Paul Jurisch, Jivka Ovtcharova
Discover Artificial Intelligence.
Published: 2024-07-01
From feed: (TITLE(ai PRE/3 sustainability))

Abstract Sustainability has become a critical global concern, focusing on key environmental goals such as achieving net-zero emissions by 2050, reducing waste, and increasing the use of recycled materials in products. These efforts often involve companies striving to minimize their carbon footprints and enhance resource efficiency. Artificial intelligence (AI) has demonstrated significant potential in tackling these sustainability challenges. This study aims to evaluate the various aspects that must be considered when deploying AI for sustainability solutions. Employing a SWOT analysis methodology, we assessed the strengths, weaknesses, opportunities, and threats of 70 research articles associated with AI in this context. The study offers two main contributions. Firstly, it presents a detailed SWOT analysis highlighting recent advancements in AI and its role in promoting sustainability. Key findings include the importance of data availability and quality as critical enablers for AI’s effectiveness in sustainable applications, and the necessity of AI explainability to mitigate risks, particularly for smaller companies facing financial constraints in adopting AI. Secondly, the study identifies future research areas, emphasizing the need for appropriate regulations and the evaluation of general-purpose models, such as the latest large language models, in sustainability initiatives. This research contributes to the growing body of knowledge on AI’s role in sustainability by providing insights and recommendations for researchers, practitioners, and policymakers, thus paving the way for further exploration at the intersection of AI and sustainable development.

Source | Open access

Benedikt Zönnchen, Charlotte Böhm, Gudrun Socher
International Conference on Higher Education Advances.
Published: 2024-06-18
From feed: (TITLE(ai PRE/3 sustainability))

The ‘digitalization lab sustAInability’ represents an educational initiative, bridging the realms of artificial intelligence (AI) and sustainability within an interdisciplinary, cross-university framework developed collaboratively by experts in computer science, political, social and economic sciences. The primary learning objective is to weave AI into the three dimensions of sustainable development – economy, society, and ecology – fostering a trans- and interdisciplinary understanding. This paper introduces a pioneering educational framework, focusing on how this course equips students as key architects of a sustainable future. Emphasizing the need for future competencies in sustainability of AI and AI for sustainability, the program not only educates but also acts as a platform for these critical issues across the involved universities and beyond. This program cultivates students who are equipped to handle the complex interplay between technology and sustainability in their future professional roles, contributing to societal, economic, and environmental advancement.

Source | Open access

Federica Lucivero
Handbook on Public Policy and Artificial Intelligence
Published: 2024-06-12
From feed: (TITLE(ai PRE/3 sustainability))

While AITs promise to optimize supply chains, circular economies, and renewable energy, they also incur significant environmental costs which are often overlooked in policy debates. The chapter discusses the concept of “digital pollution” to emphasize the physical and ecological impacts of AI infrastructures, data storage, resource consumption, and toxic emissions. It then underscores the limitations of conventional cost-benefit analyses in assessing AI’s environmental effects and calls for a value-based and political approach. The chapter emphasizes the need to transparently evaluate who benefits from or is harmed by AI’s environmental impact and to allocate responsibilities accordingly. While AI can contribute to sustainable development, its environmental costs and impacts must be addressed beyond the current fragmented and often market-driven approaches. Critical reflections on these issues are essential for guiding a global governance project that effectively considers a multitude of values, stakeholders, and regulatory mechanisms to ensure the sustainability of AI technologies.

Source

Naim A.
Harnessing High Performance Computing and AI for Environmental Sustainability
Published: 2024-05-15
From feed: (TITLE(ai PRE/3 sustainability))

written by Naim A. Published by Harnessing High Performance Computing and AI for Environmental Sustainability

Source

Shailesh Tripathi, Nadine Bachmann, Manuel Brunner, Ziad Rizk, Herbert Jodlbauer
Journal of Big Data.
Published: 2024-05-06
From feed: (TITLE(ai PRE/3 sustainability))

Abstract The United Nations’ 17 Sustainable Development Goals stress the importance of global and local efforts to address inequalities and implement sustainability. Addressing complex, interconnected sustainability challenges requires a systematic, interdisciplinary approach, where technology, AI, and data-driven methods offer potential solutions for optimizing resources, integrating different aspects of sustainability, and informed decision-making. Sustainability research surrounds various local, regional, and global challenges, emphasizing the need to identify emerging areas and gaps where AI and data-driven models play a crucial role. The study performs a comprehensive literature survey and scientometric and semantic analyses, categorizes data-driven methods for sustainability problems, and discusses the sustainable use of AI and big data. The outcomes of the analyses highlight the importance of collaborative and inclusive research that bridges regional differences, the interconnection of AI, technology, and sustainability topics, and the major research themes related to sustainability. It further emphasizes the significance of developing hybrid approaches combining AI, data-driven techniques, and expert knowledge for multi-level, multi-dimensional decision-making. Furthermore, the study recognizes the necessity of addressing ethical concerns and ensuring the sustainable use of AI and big data in sustainability research.

Source | Open access

Isabel Fischer, Priyanka Chhaparia, Bimal Arora, Lutz Preuss, Marie-Dolores Ako-Adounvo
Itnow
Published: 2024-05-01
From feed: (TITLE(ai PRE/3 sustainability))

Abstract While generative AI tools are good at replicating patterns to generate text, fully replicating human reasoning around sustainability is a whole other ball game. A team of five academic writers have their say.

Source

Shanmuga Pria, Iman Al Rubaie, V. Madhusudhan Prasad
Risks and Challenges of AI Driven Finance Bias Ethics and Security
Published: 2024-04-19
From feed: (TITLE(ai PRE/3 sustainability))

In recent years, the imperative for businesses to integrate Environmental, Social, and Governance (ESG) factors into their decision-making processes has become increasingly evident, reflecting a broader societal shift towards sustainable practices. This transition is driven by a recognition of the interconnectedness between business operations and environmental and social impacts, to create long-term value for all stakeholders. The framework underpinning AI-driven integration elucidates how machine learning algorithms and natural language p To address these challenges, the framework offers recommendations for policymakers and regulatory bodies to promote the adoption of AI-driven integration for ESG factors. By fostering an enabling environment that incentivizes sustainability-oriented decision-making, policymakers can accelerate the transition towards a more sustainable and resilient economy. By embracing AI technologies, organizations can navigate the complexity of ESG factors

Source

Matthias C. Rillig, Atoosa Kasirzadeh
Environmental Science and Technology.
Published: 2024-04-18
From feed: (TITLE(ai PRE/3 sustainability))

I n the near future, households and businesses may increasingly rely on advanced AI personal assistants (AIPAs) to manage their daily operations, with potentially transformative effects on energy use, carbon emissions, and sustainability. AIPAs are digital agents powered by generative AI, such as large language models (LLMs) like those that underpin ChatGPT, which can produce human-like text, image, and video. 1 These AI-driven assistants interact with users through natural language interfaces, translating human requests into computer commands to manage various devices, tasks, and applications. urrent AIPAs, such as Amazon's Alexa, Microsoft's Cortana, Google Assistant, or Apple's Siri, are designed to assist with a relatively narrow set of tasks, and as such, they are far cry from Joi, the holographic and immensely capable AIPA featured in the movie "Blade Runner 2049". However, as the rapid technological development in generative AI continues, future AIPAs may become capable of mastering highly complex tasks that involve content creation, device integration, communication, coaching, real-time information retrieval and

Source | Open access

Ilkhom Abbosovich Nosirov, Ilmidin Toshmatovich Yormatov, Nilufar Yuldasheva, Feruza Avulchayeva
2024 International Conference on Knowledge Engineering and Communication Systems Ickecs 2024
Published: 2024-04-18
From feed: (TITLE(ai PRE/3 sustainability))

The integration of Artificial Intelligence (AI) within corporate sectors has ushered in a transformative era for sustainability practices, redefining environmental and social governance (ESG) strategies. This comprehensive survey explores the multifaceted impacts of AI on corporate sustainability, examining both the potential benefits and challenges. By leveraging diverse AI technologies such as machine learning, natural language processing, and robotics, companies can enhance their sustainability efforts through improved efficiency, reduced waste, and innovative solutions to complex environmental problems. However, the deployment of AI also presents significant challenges, including energy consumption, ethical considerations, and the socio-economic implications of automation. Through a detailed analysis of current technologies, this paper employs mathematical expressions to quantify the environmental benefits of AI integration, including algorithms that optimize resource use and reduce carbon footprints. Comparative results highlight the effectiveness of various AI applications in achieving sustainability goals, supported by graphs and statistical analysis. Moreover, the study delves into case studies of successful AI implementation in sectors such as energy, manufacturing, and waste management, providing a holistic view of AI’s role in promoting corporate sustainability. By balancing the technological advancements with ethical and environmental considerations, this survey underscores the critical need for responsible AI integration in the pursuit of a more sustainable and equitable corporate future.

Source

Francesco Pistolesi, Beatrice Lazzerini
IEEE Transactions on Industrial Informatics.
Published: 2023-05-01
From feed: (TITLE(ai PRE/3 sustainability))

The papers in this special issue focus on artificial intelligence (AI) for efficiency and sustainability in assembly/disassembly industrial processes. Assembly and disassembly lines are the backbone of modern manufacturing. These lines transform raw materials into finished products or take apart used products for reuse and recycling. Assembly lines are designed to optimize the process of putting together products, ensuring that each part is in the right place at the right time. Conversely, disassembly lines are designed to reverse this process, breaking down products into their components for reuse or recycling. Improving these lines is becoming more critical with the increasing complexity of products and the need for higher speed and greater efficiency.

Source | Open access

Felix Zechiel, Marah Blaurock, Ellen Weber, Marion Büttgen, Kristof Coussement
Industrial Marketing Management.
Published: 2024-04-17
From feed: (TITLE(ai PRE/3 sustainability))

Sustainability is at the top of the agenda of most tech companies. Specifically, tech companies increasingly utilize artificial intelligence (AI) to meet their sustainability goals. However, little is known about how tech companies can leverage AI to accelerate sustainability by formulating and implementing appropriate strategies. To better understand the intertwined nature of AI and sustainability from a strategy perspective, this research conceptually develops a novel AI x Sustainability framework by drawing from the nested sustainability model and integrating insights from different literature streams. It then applies this framework to six leading Big Tech companies (i.e., Amazon, Google, IBM, Meta, Microsoft, and SAP) by conducting a comprehensive document analysis of 69 documents describing 244 individual AI x Sustainability initiatives to reveal whether and how these companies appear to follow specific AI x Sustainability strategies. Lastly, an exploratory survey with potential tech companies' clients (N = 192) sheds light on how clients perceive tech companies' communicated strategic positioning based on the framework. The research provides new theoretical insights, serves as a blueprint for other tech companies, including implications for their AI x Sustainability positioning, and offers a variety of future research directions.

Source | Open access

Evangelos Katsamakas
Sustainability Switzerland.
Published: 2024-04-15
From feed: (TITLE(ai PRE/3 sustainability))

Sustainability and its connection to digital technology have attracted significant interest in business [...]

Source | Open access

Reenu Kumari, Komal Sharma, Rajesh Kumar
Social and Ethical Implications of AI in Finance for Sustainability
Published: 2024-04-05
From feed: (TITLE(ai PRE/3 sustainability))

Artificial intelligence (AI) is becoming an inseparable part of our daily lives as it can solve tough problems in a competent way in manifold areas like bank, insurance, healthcare, education, operations, etc. This chapter explains how financial institutions are implementing AI, algorithm trading, and intelligence that is adaptive to their financial processes. The banking sector in India has gone through significant transformations with the infusion of technology. Technological innovation has played a crucial role in reshaping the landscape of banking operations, bringing about positive changes in various aspects such as cost effectiveness, productivity and efficiency, small value transactions, digital payment systems, mobile banking apps, and online transfers. Adoption of technology has given customers a wide range of choices in terms of banking services from online banking to mobile apps.

Source

Derbali, Abdelkader Mohamed Sghaier 1985-
Social and Ethical Implications of AI in Finance for Sustainability
Published: 2024-04-05
From feed: (TITLE(ai PRE/3 sustainability))

"The crucial challenge of integrating sustainability into business and investment decisions is compounded by the complexity of analyzing vast and intricate datasets to make informed choices. Traditional approaches often fail to provide timely and accurate insights into environmental, social, and governance (ESG) factors, hindering progress toward a greener future. Additionally, the rapid evolution of AI and machine learning in finance has left many professionals needing help to grasp their full potential in advancing sustainability goals. With a comprehensive understanding and practical guidance, organizations can stay caught up in adopting sustainable practices and leveraging AI for financial and environmental benefits.Social and Ethical Implications of AI in Finance for Sustainability offers a timely and comprehensive solution to these challenges by thoroughly examining how AI can safely enhance sustainability in finance. The book bridges the gap between theory and practice, offering practical insights and real-world applications to empower academics, practitioners, policymakers, and students. Through a series of expertly curated chapters, readers will gain a deep understanding of the role AI plays in reshaping finance for a sustainable future. The book's instructional elements, including case studies and expert analysis, provide a roadmap for incorporating AI into sustainability strategies, enabling organizations to make informed decisions and drive positive change."--

Source

Galena Pisoni, Bálint Molnár
International Journal of Knowledge Management.
Published: 2024-03-20
From feed: (TITLE(ai PRE/3 sustainability))

Many companies look for novel ways to trace their operational sustainability. The application of AI to analyze and make sense of the big data the company holds represents one promising approach for this aim. The authors study how one can set and design an AI-based solution for improving the sustainability of complex business processes and decision-making in companies of different types. First, they provide a general analysis of current frameworks for measuring adherence to sustainability goals for companies, then they present a conceptual framework and architecture design for an AI-enabled sustainability service for companies. The implications of our research suggest that AI can provide distinct functions: (a) automation: taking big data from different departments and analyzing them with the aim of tracing the sustainability of the company; (b) support: to help decision-making and create relevant insights for stakeholders that are coherent with defined sustainability decision criteria. To the authors' knowledge, no previous research has provided analysis and design of such AI solution for companies.

Source | Open access

R. Anitha, M. Rajkumar, B. Jothi, H. ‎Mickle Aancy, G. Sujatha, Sam Ban
Advanced Applications in Osmotic Computing
Published: 2024-03-04
From feed: (TITLE(ai PRE/3 sustainability))

The integration of technology in healthcare presents both opportunities and challenges. This chapter explores the relationship between technology and healthcare, emphasizing the need for security, ethical standards, and social implications. It examines vulnerabilities in digitalizing healthcare data, highlighting the importance of robust encryption methods, access controls, and cybersecurity frameworks to protect sensitive patient information and ensure data confidentiality, integrity, and availability. The chapter discusses the ethical implications of technology integration in healthcare, focusing on data privacy, informed consent, AI-driven decision-making, and responsible technology use. It proposes ethical frameworks to foster trust and transparency while addressing social implications like accessibility, equity, and the digital divide. The chapter advocates for a comprehensive approach that combines technological advancements with strict security measures, ethical guidelines, and social awareness, urging multidisciplinary collaboration to maximize benefits and mitigate risks.

Source

Naiara Uriarte‐Gallastegi, Germán Arana Landín, Beñat Landeta‐Manzano, Iker Laskurain‐Iturbe
Energies.
Published: 2024-01-30
From feed: (TITLE(ai PRE/3 sustainability))

This research addresses the increasing importance of understanding how Artificial Intelligence can facilitate the transition of companies to a Circular Economy model. This study focuses on energy management, examining its impact on efficiency and emissions across a multi-case analysis of 18 projects in diverse sectors. The findings indicate that Artificial Intelligence positively influences both variables, with variations across applications and sectors. Notably, Artificial Intelligence significantly enhances energy efficiency in four out of six sectors, achieving over 5% improvement in half of the projects. Regarding emissions, positive effects are observed in 15 out of 18 projects, resulting in over 5% reductions in seven cases. Artificial Intelligence plays a pivotal role in emissions reduction in the Design and Energy sectors, with some projects achieving over 20% reductions. Additionally, this study explores how improved energy efficiency positively affects strategic business variables, such as cost, quality, and delivery time. The impact on emissions contributes to reducing occupational risks, particularly those associated with chemical and biological agents. Although managers are satisfied, measures need to be taken to overcome the lack of employee acceptance. These findings are of great interest to the stakeholders involved in the integration of Artificial Intelligence into companies.

Source | Open access

Jennifer Garard, A. Cohen, Ernest Habanabakize, Erin Gleeson, Mélisande Teng, Gaétan Marceau Caron, Daoud Piracha, Rosette Lukonge Savanna, Kinsie Rayburn, Melissa Rosa, Kaspar Kundert, Éliane Ubalijoro
Human Centered AI A Multidisciplinary Perspective for Policy Makers Auditors and Users.
Published: 2024-01-25
From feed: (TITLE(ai PRE/3 sustainability))

There is broad consensus that global sustainability crises such as climate change, biodiversity loss, and soil degradation are already causing severe negative impacts on people and the planet that will worsen over time. Addressing these complex, interrelated issues is not straightforward. The Intergovernmental Panel on Climate Change has made it clear that tackling climate change will require rapid and unprecedented changes across all sectors of society (IPCC, 2018) and that “the move towards climate-resilient societies requires transformational or deep systemic change” (Pathak et al., 2022). The Kunming-Montreal Global Biodiversity Framework plainly articulates the urgent need to reduce threats to biodiversity, for example, by phasing out harmful subsidies and enhancing incentives for the sustainable use of biodiversity (CBD COP-15, 2022). The United Nations Food and Agriculture Organization warns that rapidly degrading soils arising from intensive agricultural practices pose an immense threat to food security and ecosystems around the world (FAO, 2022).

Source | Open access

Sami Fattouch, Fethi Ben Slama, H. Jamoussi, Luana Bontempo
Fostering Cross Industry Sustainability with Intelligent Technologies
Published: 2024-01-22
From feed: (TITLE(ai PRE/3 sustainability))

The literature is prolific in studies about the artificial intelligence (AI) applications, particularly to support formal education and develop adaptive lifelong learning environments by means of an array of flexible, inclusive, and interactive tools. AI-based education is assumed to be determinant for the achievement of the Sustainable Development Goals (SDGs), targeted for 2030, and supported by all 193 member states of the United Nations. This intelligent technology is expected to play a key role to raising awareness about the management of food discards and byproducts for a circular economy, thus optimizing the resources' efficiency and slowing down their economic, social, and environmental impacts. This chapter provides an overview of the AI applications and challenges to promote sustainable food habits and to monitor and manage local food byproducts suitable for human consumption to develop nutritional and healthy added-value outcomes.

Source

Yu, Poshan, Popescu, Cristina Raluca Gh.
Intersecting Environmental Social Governance and AI for Business Sustainability
Published: 2024-01-16
From feed: (TITLE(ai PRE/3 sustainability))

written by Yu, Poshan, Popescu, Cristina Raluca Gh. Published by Intersecting Environmental Social Governance and AI for Business Sustainability

Source

Cristina Raluca Gh. Popescu, Arturo Luque González
Intersecting Environmental Social Governance and AI for Business Sustainability.
Published: 2024-01-16
From feed: (TITLE(ai PRE/3 sustainability))

Investigating the unprecedented and not yet fully known impact of artificial intelligence (AI) for business sustainability in digital ecosystem implicates acknowledging and reshaping the pivotal role played by the environmental social governance (ESG) framework used to assess efficiency, performance, and international strategies in terms of business practices. What is more, achieving the Sustainable Development Goals (SDGs) by benefiting from guidance from ESG, as well as intelligence demonstrated by computers, may present both opportunities and risks, depending on the perspectives taken into consideration. Furthermore, building a culture of sustainability and developing successful sustainable business models have the amazing power of fostering climate justice, restoring damaged ecosystems, and developing action plans capable of offering immediate responses to climate change. Can individuals' quality of life and standards of living boost the overall well-being of societies when businesses are becoming more sustainable? Are the global living standards moving in the right direction?

Source | Open access

Gianluca Brunori, Manlio Bacco, Silvia Rolandi
Routledge Handbook of Sustainable Diets
Published: 2022-11-18
From feed: (TITLE(ai PRE/3 sustainability))

This chapter discusses one of the most urgent and significant challenges we must face—the transition towards sustainable food systems. In this context, we discuss the role digital technologies may play, proposing the use of socio-cyber-physical systems as a paradigm, and extension of its Information and Communications Technology version, the cyber-physical system. Key digital technologies, with the potential of being game changers, are identified, as is their role in supporting a transition towards greater sustainability. Risks are identified and discussed related to the adoption of digital technologies in the food system, as well as policy conditions for digital technologies to operate in societies’ interests. Recommendations are provided on the embodiment of socio-economic principles in the digitalisation process, in line with the socio-cyber-physical approach.

Source

Glazkova Valeriya, Madhu Kirola, Manish Gupta, P. Shyamala Bharathi, Puja Acharya
Bio Web of Conferences.
Published: 2024-01-01
From feed: (TITLE(ai PRE/3 sustainability))

In the context of Industry 5.0, this long-term study assesses the significant influence of AI-based sustainability metrics. It also illuminates a novel paradigm in which artificial intelligence (AI) and human expertise work together to jointly drive sustainability, financial performance, employee satisfaction, and overall ecological responsibility. AI-driven sustainability efforts produced a surprising 12% reduction in trash creation, an amazing 7% reduction in energy usage, and an 8% drop in CO2 emissions over a five-year period. Financially speaking, these actions showed up as a steady 4% annual revenue growth, $2 million in cost reductions on average each year, and a cumulative 3.4% gain in return on investment. The human factor is even more notable, with employee satisfaction ratings rising from 4.2 to 4.7 and work-life balance scores significantly rising from 4.1 to 4.6. By 2024, 70% of workers will have adopted AI, demonstrating how essential AI has become to the working. An all-encompassing sustainability score that included these dynamic components increased from 60 to 75 in 2024, indicating a general improvement in sustainability. This study emphasizes the mutually beneficial relationship between artificial intelligence (AI) and sustainability in Industry 5.0. It shows how AI fosters a sustainable and balanced industrial future by improving environmental responsibility and workforce satisfaction while also producing significant financial benefits.

Source | Open access

Edward Nkadimeng, Thuso Mathaha
Soft Computing in Industry 5 0 for Sustainability
Published: 2024-01-01
From feed: (TITLE(ai PRE/3 sustainability))

written by Edward Nkadimeng, Thuso Mathaha Published by Soft Computing in Industry 5 0 for Sustainability

Source

A. Arun Kumar, Rakesh Suryadevara, T. Sowmyya, Gowri B. Chanal
Approaches to Global Sustainability Markets and Governance
Published: 2024-01-01
From feed: (TITLE(ai PRE/3 sustainability))

written by A. Arun Kumar, Rakesh Suryadevara, T. Sowmyya, Gowri B. Chanal Published by Approaches to Global Sustainability Markets and Governance

Source

Francesco Paolo Appio, Federico Platania, Celina Toscano Hernandez
IEEE Transactions on Engineering Management
Published: 2024-01-01
From feed: (TITLE(ai PRE/3 sustainability))

In this article, we investigate the role of artificial intelligence (AI) as a strategic catalyst for sustainable entrepreneurship, focusing on the twin transition to sustainable and digital economies. By analyzing AI patents and identifying key thematic clusters around sustainability issues, the research illustrates the AIs potential as both a technological and strategic asset in advancing sustainable development goals. The findings offer a novel perspective on how AI facilitates sustainable business practices and innovation, emphasizing its critical role in bridging technology and sustainability. This comprehensive analysis contributes to the theoretical landscape and offers practical insights for scholars, practitioners, and policymakers, highlighting AIs transformative impact on achieving global sustainability goals through entrepreneurial initiatives.

Source

Fanar Shwedeh, Said A. Salloum, Ahmad Aburayya, Brihan Fatin, Mohamed Ahmed Elbadawi, Zainab Al Ghurabli, Tamadher Al Dabbagh
Studies in Big Data
Published: 2024-01-01
From feed: (TITLE(ai PRE/3 sustainability))

written by Fanar Shwedeh, Said A. Salloum, Ahmad Aburayya, Brihan Fatin, Mohamed Ahmed Elbadawi, Zainab Al Ghurabli, Tamadher Al Dabbagh Published by Studies in Big Data

Source

Maarten Voorneveld
International Conference on Enterprise Information Systems Iceis Proceedings.
Published: 2024-01-01
From feed: (TITLE(ai PRE/3 sustainability))

written by Maarten Voorneveld Published by International Conference on Enterprise Information Systems Iceis Proceedings.

Source | Open access

Christian Zinke-Wehlmann
Informatik Aktuell.
Published: 2024-01-01
From feed: (TITLE(ai PRE/3 sustainability))

Abstract In the contemporary debate, surrounding the future of work and life, Artificial Intelligence (AI), resilience, and sustainability have emerged as pivotal concepts. Within the industrial realm, their collective convergence is driving unprecedented transformative shifts, challenging traditional paradigms. This positioning paper delves into the intricate interlinkages binding these three paradigms. Examples such as AI-driven automation, enhancing efficiency, and predictive maintenance, reducing machinery downtime, underscore the transformative role of AI in the industry. Meanwhile, an increasing emphasis on environmental responsibility highlights the growing importance of sustainability in the industrial sector. Resilience, embodied through the ability to withstand crises and maintain strong supply chains, is equally essential. The article also delves deep into the specific relations between AI, sustainability and resilience. By weaving these concepts together, the paper aims to provide a holistic perspective on the interconnectedness, emphasizing the need for a balanced approach in the modern industry to ensure not only technological advancement but also a resilient and sustainable future.

Source | Open access

Cantarini P.
Revista Juridica
Published: 2024-01-01
From feed: (TITLE(ai PRE/3 sustainability))

written by Cantarini P. Published by Revista Juridica

Source

Ishneet Kaur Dua, Parth Patel
Optimizing Generative AI Workloads for Sustainability Balancing Performance and Environmental Impact in Generative AI.
Published: 2024-01-01
From feed: (TITLE(ai PRE/3 sustainability))

written by Ishneet Kaur Dua, Parth Patel Published by Optimizing Generative AI Workloads for Sustainability Balancing Performance and Environmental Impact in Generative AI.

Source | Open access

Mostafa Al‐Emran, Bassam Abu-Hijleh, AbdulRahman A. Alsewari
IEEE Transactions on Engineering Management.
Published: 2024-01-01
From feed: (TITLE(ai PRE/3 sustainability))

The swift progress of generative artificial intelligence (AI) tools offers remarkable potential for revolutionizing educational methods and enhancing social sustainability. Despite its potential, understanding the factors driving its adoption and how that affects social sustainability remains underexplored. This study aims to address this gap by integrating AI attributes (“perceived anthropomorphism,” “perceived intelligence,” and “perceived animacy”) with the theory of planned behavior and the technology-environmental, economic, and social sustainability theory (T-EESST) to develop a theoretical research model. Utilizing a hybrid structural equation modeling and artificial neural network approach, we analyzed data collected from 1048 university students to evaluate the developed model. Our findings revealed that while perceived behavioral control has an insignificant impact on generative AI use, attitudes emerge as the most critical factor, further reinforced by the significant role of subjective norms. Perceived anthropomorphism, perceived intelligence, and perceived animacy were also found to influence students’ attitudes significantly. More importantly, the findings supported the role of generative AI in positively affecting social sustainability, aligning with the principles of T-EESST. This study's significance lies in its holistic examination of the interplay between technological attributes, motivational aspects, and sustainability outcomes, offering valuable insights for various stakeholders.

Source | Open access

Pajila B.
Innovations in Green and Energy Efficient Warehousing
Published: 2024-01-01
From feed: (TITLE(ai PRE/3 sustainability))

written by Pajila B. Published by Innovations in Green and Energy Efficient Warehousing

Source

Dharmendra Kr Dubey, Mohit Mishra, Amit Kumar Pathak, Arjit Tomar, Harish Chandra Maurya
Smart Innovation Systems and Technologies
Published: 2024-01-01
From feed: (TITLE(ai PRE/3 sustainability))

written by Dharmendra Kr Dubey, Mohit Mishra, Amit Kumar Pathak, Arjit Tomar, Harish Chandra Maurya Published by Smart Innovation Systems and Technologies

Source

A Mason Sara, Ait Kbir M’hamed, Ahsain Soulaimane
Lecture Notes in Networks and Systems
Published: 2024-01-01
From feed: (TITLE(ai PRE/3 sustainability))

written by A Mason Sara, Ait Kbir M’hamed, Ahsain Soulaimane Published by Lecture Notes in Networks and Systems

Source

Sucharita Gopal, J.P. Pitts
Sustainable Finance.
Published: 2024-01-01
From feed: (TITLE(ai PRE/3 sustainability))

written by Sucharita Gopal, J.P. Pitts Published by Sustainable Finance.

Source | Open access

Gundeti R.
Exploring Ethical Dimensions of Environmental Sustainability and Use of AI
Published: 2023-12-07
From feed: (TITLE(ai PRE/3 sustainability))

written by Gundeti R. Published by Exploring Ethical Dimensions of Environmental Sustainability and Use of AI

Source

Esmaeil Najafi, Iman Atighi
Information Logistics for Organizational Empowerment and Effective Supply Chain Management
Published: 2023-12-05
From feed: (TITLE(ai PRE/3 sustainability))

Artificial intelligence and machine learning are overcoming more businesses and distinctive angles of our lives daily. Of course, the coordination industry isn't absolved from this. Manufactured insights and machine learning within the coordination industry can play a vast and successful part in the field of the supply chain. By utilizing this innovation, forms can be optimized, botches made by people can be maintained a strategic distance from, and future openings and challenges can be anticipated. In this manner, business productivity and success will be given. In this chapter, subtle elements are mentioned about the benefits of utilizing and executing manufactured intelligence technology within the supply chain, and by perusing these things, you may get the significance of how counterfeit intelligence and machine learning calculations can offer assistance in creating your commerce.

Source

Joshua Doe, Robert Ebo Hinson
Journal of Family Business Management
Published: 2023-12-04
From feed: (TITLE(ai PRE/3 sustainability))

Purpose Artificial intelligence (AI) and sustainable business represent the irrefutable future of all forward looking businesses in the world today. In this perspective article, the authors explore the confluence of these important topics by highlighting the role of family businesses in advancing sustainable brand activism aligned with the United Nations Sustainable Development Goals (UNSDGs), like SDG 1, which emphasises poverty eradication. The authors fall on the transformative potential of artificial intelligence (AI) and online brand communities in family businesses as an anchor for promoting sustainability practices that align with UNSDGs. Design/methodology/approach Using literature review, the authors fall on the transformative potential of AI and online brand communities in family businesses as an anchor for promoting sustainability practices that align with UNSDGs. Findings Scholarly research on AI-driven sustainability brand activism in family businesses is either limited or nonexistent. Family businesses have a unique opportunity to use AI for eco-friendly operations, personalised brand engagement, eco-friendly product development, global collaborations and education and advocacy in support of the UNSDGs. Future research could look at how family businesses align their values, their long-term effects, how they work across generations, how resilient and flexible they are and how they compare to non-family businesses when it comes to using AI and brand activism as long-term strategies for sustainability and survival. Originality/value The authors call for family businesses, governments and stakeholders to take theoretical and practical actions in promoting AI-driven sustainability brand activism aligned with the UNSDGs. It underscores the distinctive role of family businesses in driving sustainability and fostering brand activism through AI in a digital age.

Source

Nancy Diana Panța, Nelu-Eugen Popescu
Studies in Business and Economics.
Published: 2023-12-01
From feed: (TITLE(ai PRE/3 sustainability))

Abstract Artificial intelligence (AI) sparked the attention of both researchers and the business community worldwide and has become a buzzword. Similarly, (business) sustainability emerged as a prominent and pivotal concept. Given the rapid evolution of the technological advancement in AI and its potential impact(s), this paper aims to identify the ways in which AI crosses paths with business sustainability, to provide an overview of the topic and to uncover research trends using a bibliometric approach. In order to reach the research goal of the paper, we investigated the academic literature published and indexed in Scopus database using computer assisted quantitative techniques on bibliometric data and with the help of VOSviewer we visually emphasized the interconnections between fields and results. Ultimately, the present paper intends to contribute to a deeper understanding of the symbiotic relationship between AI and business sustainability, by providing insights that are purposed to enhance the academic discourse in a rapidly evolving domain.

Source | Open access

Hatem A. Alharbi, Khulud K. Alharbi, Ch Anwar Ul Hassan
Sustainability Switzerland.
Published: 2023-11-07
From feed: (TITLE(ai PRE/3 sustainability))

In the realm of sustainable IoT and AI applications for the well-being of elderly individuals living alone in their homes, falls can have severe consequences. These consequences include post-fall complications and extended periods of immobility on the floor. Researchers have been exploring various techniques for fall detection over the past decade, and this study introduces an innovative Elder Fall Detection system that harnesses IoT and AI technologies. In our IoT configuration, we integrate RFID tags into smart carpets along with RFID readers to identify falls among the elderly population. To simulate fall events, we conducted experiments with 13 participants. In these experiments, RFID tags embedded in the smart carpets transmit signals to RFID readers, effectively distinguishing signals from fall events and regular movements. When a fall is detected, the system activates a green signal, triggers an alarm, and sends notifications to alert caregivers or family members. To enhance the precision of fall detection, we employed various machine and deep learning classifiers, including Random Forest (RF), XGBoost, Gated Recurrent Units (GRUs), Logistic Regression (LGR), and K-Nearest Neighbors (KNN), to analyze the collected dataset. Results show that the Random Forest algorithm achieves a 43% accuracy rate, GRUs exhibit a 44% accuracy rate, and XGBoost achieves a 33% accuracy rate. Remarkably, KNN outperforms the others with an exceptional accuracy rate of 99%. This research aims to propose an efficient fall detection framework that significantly contributes to enhancing the safety and overall well-being of independently living elderly individuals. It aligns with the principles of sustainability in IoT and AI applications.

Source | Open access

Haiyan Kong, Zihan Yin, Yehuda Baruch, Yue Yuan
Journal of Vocational Behavior
Published: 2023-09-30
From feed: (TITLE(ai PRE/3 sustainability))

written by Haiyan Kong, Zihan Yin, Yehuda Baruch, Yue Yuan Published by Journal of Vocational Behavior

Source

Vasim Ahmad, Lalit Goyal, Madhu Arora, Rakesh Kumar, Kanegonda Ravi Chythanya, Shreya Chaudhary
Proceedings of International Conference on Contemporary Computing and Informatics Ic3i 2023
Published: 2023-09-14
From feed: (TITLE(ai PRE/3 sustainability))

Sustainability reporting has gained significant importance in recent years, with organizations increasingly recognizing the need to report on their environmental, social, and governance (ESG) performance. The traditional methods of collecting and reporting ESG data are often manual and time-consuming, leading to errors and inconsistencies. The emergence of Accounting, which integrates emerging technologies such as artificial intelligence (AI), blockchain, big data analytics, and robotic process automation (RPA) into the accounting profession, provides an opportunity to enhance the quality and effectiveness of sustainability reporting. The integration of AI in sustainability reporting has several potential benefits, including the automation of ESG data collection and analysis, the identification of trends and patterns in ESG data, and the provision of real-time insights into sustainability performance. This research paper investigates the impact of AI on sustainability reporting in Accounting.

Source

Naoki Abe, Kathleen Buckingham, Yuzhou Chen, Bistra Dilkina, Emre Eftelioglu, Auroop R. Ganguly, Yulia R. Gel, James Hodson, Ramakrishnan Kannan, Huikyo Lee, Jiafu Mao, Rose Yu
Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.
Published: 2023-08-04
From feed: (TITLE(ai PRE/3 sustainability))

The Fragile Earth Workshop is a recurring event in ACM's KDD Conference on research in knowledge discovery and data mining that gathers the research community to find and explore how data science can measure and progress climate and social issues, fol- lowing the United Nations Sustainable Development Goals (SDGs) framework.

Source | Open access

Tamar Eilam, Pedro D. Bello-Maldonado, Bishwaranjan Bhattacharjee, Carlos Costa, Eun Kyung Lee, Asser Tantawi
2nd Workshop on Sustainable Computer Systems Hotcarbon 2023.
Published: 2023-07-09
From feed: (TITLE(ai PRE/3 sustainability))

Recently, we are witnessing truly groundbreaking achievements using AI models, such as the much talked about generative large language models, the broader area of foundation models, and the wide range of applications with a tremendous potential to accelerate scientific discovery, and enhance productivity. AI models and their use are growing at a super-linear pace. Inference jobs are measured by the trillions, and model parameters by the billions. This scaling up comes with a tremendous environmental cost, associated with every aspect of models' life cycle: data preparation, pre-training, and post deployment re-training, inference, and, the embodied emission of the systems used to support the execution. There is an urgent need for the community to come together and conduct a meaningful conversation about the environmental cost of AI. To do that, we ought to develop an agreed upon set of metrics, methodology, and framework to quantify AI's sustainability cost in a holistic and complete fashion. In this paper, we propose unified AI Sustainability metrics that can help foster a sustainability mind-set and enable analysis, and strategy setting. To do that, we build on and extend the data center sustainability metrics, defined in [19], by introducing (for the first time to our knowledge) the concept of embodied product emission (EPC) to capture the creation cost of software assets, such as software platforms, models, and data-sets. We then use this new concept to expand the job sustainability cost metrics (JCS and ASC) offered in [19] to factor in the context of the execution of jobs, such as a platform as-a-service, or a model serving inference jobs. The result is applicable to any data center job, not just for AI, and contributes towards accuracy and completeness. We then show how to apply this approach to AI, with a particular focus on the entire life cycle, including all phases of the life cycle, as well as the provenance of models, where one model is used (distilled) to create another one. We demonstrate how the metric can be used to inform a more meaningful debate about AI strategies and cost. Te novelty of the approach is that it can be used to reason about strategies and trade-offs across the life cycle and 'supply-chain' of models.

Source | Open access

Daniela Inclezan, Luis I. Prádanos
Impact of Artificial Intelligence in Business and Society Opportunities and Challenges
Published: 2023-06-12
From feed: (TITLE(ai PRE/3 sustainability))

AI intensifies existing trends towards ecological decline through its high dependence on materials and energy, as well as its development in the context of a profit-maximizing and growth-oriented economic culture. As the vast majority of machine learning systems, even those focused on Sustainable Development Goals, are designed to make processes faster and more efficient and focus on symptoms rather than root causes, they are accelerating ecological depletion, energy decline, material extraction, labor exploitation, and global inequality. To correct this situation, we advocate for a democratic process of deciding what and how AI should be implemented, so that it serves the common good and the regeneration of ecosystems.

Source

Ana Corceiro, Khadijeh Alibabaei, Eduardo Assunção, Pedro Dinis Gaspar, Nuno Pereira
Processes.
Published: 2023-04-19
From feed: (TITLE(ai PRE/3 sustainability))

The rapid growth of the world’s population has put significant pressure on agriculture to meet the increasing demand for food. In this context, agriculture faces multiple challenges, one of which is weed management. While herbicides have traditionally been used to control weed growth, their excessive and random use can lead to environmental pollution and herbicide resistance. To address these challenges, in the agricultural industry, deep learning models have become a possible tool for decision-making by using massive amounts of information collected from smart farm sensors. However, agriculture’s varied environments pose a challenge to testing and adopting new technology effectively. This study reviews recent advances in deep learning models and methods for detecting and classifying weeds to improve the sustainability of agricultural crops. The study compares performance metrics such as recall, accuracy, F1-Score, and precision, and highlights the adoption of novel techniques, such as attention mechanisms, single-stage detection models, and new lightweight models, which can enhance the model’s performance. The use of deep learning methods in weed detection and classification has shown great potential in improving crop yields and reducing adverse environmental impacts of agriculture. The reduction in herbicide use can prevent pollution of water, food, land, and the ecosystem and avoid the resistance of weeds to chemicals. This can help mitigate and adapt to climate change by minimizing agriculture’s environmental impact and improving the sustainability of the agricultural sector. In addition to discussing recent advances, this study also highlights the challenges faced in adopting new technology in agriculture and proposes novel techniques to enhance the performance of deep learning models. The study provides valuable insights into the latest advances and challenges in process systems engineering and technology for agricultural activities.

Source | Open access

Zhenghua Chen, Min Wu, Alvin Chan, Xiaoli Li, Yew-Soon Ong
IEEE Computational Intelligence Magazine
Published: 2023-04-13
From feed: (TITLE(ai PRE/3 sustainability))

Artificial Intelligence (AI) is a fast-growing research and development (R&D) discipline which is attracting increasing attention because it promises to bring vast benefits for consumers and businesses, with considerable benefits promised in productivity growth and innovation. To date, significant accomplishments have been reported in many areas that have been deemed challenging for machines, ranging from computer vision, natural language processing, audio analysis to smart sensing and many others. The technology trend in realizing success has developed towards increasingly complex and large-size AI models to solve more complex problems at superior performance and robustness. This rapid progress, however, has taken place at the expense of substantial environmental costs and resources. In addition, debates on the societal impacts of AI, such as fairness, safety, and privacy, have continued to grow in intensity. These issues have reflected major concerns pertaining to the sustainable development of AI. In this work, major trends in machine learning approaches that can address the sustainability problem of AI have been reviewed. Specifically, the emerging AI methodologies and algorithms are examined for addressing the sustainability issue of AI in two major aspects, i.e., environmental sustainability and social sustainability of AI. Then, the major limitations of the existing studies are highlighted, and potential research challenges and directions are proposed for the development of the next generation of sustainable AI techniques. It is believed that this technical review can help promote a sustainable development of AI R&D activities for the research community.

Source

Ahmed Hussein Ali
Babylonian Journal of Artificial Intelligence.
Published: 2023-04-08
From feed: (TITLE(ai PRE/3 sustainability))

As artificial intelligence continues its relentless march towards advancing capability, there is surprisingly little discussion around advancing responsibility. The data centers underpinning AI research devour massive amounts of energy and contribute substantially to emissions. But what if AI could flip the script and help curb emissions instead? An emerging field known as Green AI provides solutions by building economic and environmental sustainability directly into AI systems. In a paper published this week, researchers set out an innovative framework for leveraging machine learning to accelerate the transition to a circular economy. This model moves away from the traditional linear take-make-dispose economy towards one where products, parts, and materials can be reused, remanufactured, and recycled in closed loops. AI and automation will provide the optimization backbone to make such closed-loop supply chains efficient and cost-effective.

Source | Open access

Alessandra Toniato, Oliver Schilter, Teodoro Laino
Chimia.
Published: 2023-03-29
From feed: (TITLE(ai PRE/3 sustainability))

Sustainability is here to stay. As businesses migrate away from fossil fuels and toward renewable sources, chemistry will play a crucial role in bringing the economy to a point of net-zero emissions. In fact, chemistry has always been at the forefront of developing new or enhanced materials to fulfill societal demands, resulting in goods with appropriate physical or chemical qualities. Today, the main focus is on developing goods and materials that have a less negative impact on the environment, which may include (but is not limited to) leaving behind smaller carbon footprints. Integrating data and AI can speed up the discovery of new eco-friendly materials, predict environmental impact factors for early assessment of new technological integration, enhance plant design and management, and optimize processes to reduce costs and improve efficiency, all of which contribute to a more rapid transition to a sustainable system. In this perspective, we hint at how AI technologies have been employed so far first, at estimating sustainability metrics and second, at designing more sustainable chemical processes.

Source | Open access

Marika Salo-Lahti, Mikko Ranta
Jusletter IT
Published: 2023-01-01
From feed: (TITLE(ai PRE/3 sustainability))

People struggle with complex information everywhere: policies and prospectuses are too long, contracts and regulatory requirements confuse, and companies are required to report more and more information. Legal Design tries to tackle these problems by making information more comprehensible. AI tools, such as Open AI’s GPT-3, open up new opportunities to make readers’ and writers’ tasks easier. This paper considers how GPT-3 can help transform sustainability reporting, investment disclosures and contracts so that people and businesses can better understand their rights and obligations, address the causes of ESG problems, and monitor and strengthen sustainability.

Source

Cristian Moyano-Fernández, Jon Rueda
International Library of Ethics Law and Technology
Published: 2023-01-01
From feed: (TITLE(ai PRE/3 sustainability))

written by Cristian Moyano-Fernández, Jon Rueda Published by International Library of Ethics Law and Technology

Source

Ranjan Chaudhuri, Sheshadri Chatterjee, Demetris Vrontis, Sumana Chaudhuri
Sustainability Switzerland.
Published: 2022-10-07
From feed: (TITLE(ai PRE/3 sustainability))

The purpose of this study is to examine artificial intelligence (AI) dynamism and its impact on sustainability of firms, including small and medium enterprises (SMEs). In addition, this study investigates the moderating effects of technological and leadership support for AI technology deployment and sustainability for manufacturing and production firms. We developed a theoretical model through the lenses of expectation disconfirmation theory (EDT), technology–trust–fit (TTF) theory, contingency theory, and the knowledge contained in the existing literature. We tested the proposed theoretical model using factor-based PLS-SEM technique by analyzing data from 343 managers of SMEs. The findings of this study demonstrate that organizational characteristics, situational characteristics, technological characteristics, and individual characteristics all impacted SMEs’ deployment of AI technologies for the purpose of achieving sustainability, with technological and leadership support acting as moderators.

Source | Open access

Francesco Sovrano, Giulio Masetti
ACM International Conference Proceeding Series.
Published: 2022-10-04
From feed: (TITLE(ai PRE/3 sustainability))

The AI Act has been recently proposed by the European Commission to regulate the use of AI in the EU, especially on high-risk applications, i.e. systems intended to be used as safety components in the management and operation of road traffic and the supply of water, gas, heating and electricity. On the other hand, IEC 61508, one of the most adopted international standards for safety-critical electronic components, seem to mostly forbid the use of AI in such systems. Given this conflict between IEC 61508 and the proposed AI Act, also stressed by the fact that IEC 61508 is not an harmonised European standard, with the present paper we study and analyse what is going to happen to industry after the entry into force of the AI Act. In particular, we focus on how the proposed AI Act might positively impact on the sustainability of critical infrastructures by allowing the use of AI on an industry where it was previously forbidden. To do so, we provide several examples of AI-based solutions falling under the umbrella of IEC 61508 that might have a positive impact on sustainability in alignment with the current long-term goals of the EU and the Sustainable Development Goals of the United Nations, i.e., affordable and clean energy, sustainable cities and communities.

Source | Open access

Naoum Tsolakis, Dimitris Zissis, Spiros Papaefthimiou, Nikolaos Korfiatis
International Journal of Production Research
Published: 2021-04-25
From feed: (TITLE(ai PRE/3 sustainability))

Artificial intelligence and data analytics capabilities have enabled the introduction of automation, such as robotics and Automated Guided Vehicles (AGVs), across different sectors of the production spectrum which successively has profound implications for operational efficiency and productivity. However, the environmental sustainability implications of such innovations have not been yet extensively addressed in the extant literature. This study evaluates the use of AGVs in container terminals by investigating the environmental sustainability gains that arise from the adoption of artificial intelligence and automation for shoreside operations at freight ports. Through a comprehensive literature review, we reveal this research gap across the use of artificial intelligence and decision support systems, as well as optimisation models. A real-world container terminal is used, as a case study in a simulation environment, on Europe’s fastest-growing container port (Piraeus), to quantify the environmental benefits related to routing scenarios via different types of AGVs. Our study contributes to the cross-section of operations management and artificial intelligence literature by articulating design principles to inform effective digital technology interventions at non-automated port terminals, both at operational and management levels.

Source

Claire Nicodème
2021 IEEE Conference on Technologies for Sustainability Sustech 2021
Published: 2021-04-22
From feed: (TITLE(ai PRE/3 sustainability))

Sustainability is a vast subject involving various research areas. Emerging technologies, through their development and the possibilities they offer, impact sustainability. In particular, Artificial Intelligence and Machine Learning have been trendy research subjects for almost a decade now. They offer a wide range of potential applications, including energy efficiency and Intelligent Transportation Systems, two key elements for Smart Cities. All these developments seem to put AI in a favorable position to build Sustainability. However, AI’s own ecological impact and carbon footprint is disastrous and drastic changes must be done if this technology is to be used in a sustainable future. This paper offers a short overview of AI potential for sustainability, in the context of Smart cities. Then the drawbacks of AI energy consumption are highlighted. New ways and methodologies are proposed to create greener AI.

Source

Klaus B. Schebesch
Springer Proceedings in Business and Economics
Published: 2020-01-01
From feed: (TITLE(ai PRE/3 sustainability))

written by Klaus B. Schebesch Published by Springer Proceedings in Business and Economics

Source

Li Felländer‐Tsai
Acta Orthopaedica.
Published: 2019-10-30
From feed: (TITLE(ai PRE/3 sustainability))

Artificial intelligence (AI) embedded in healthcare technologies creates opportunities and great expectations. However, new interactions between man and machine arise and must be addressed (Lancet ...

Source | Open access

James Canton
Csr Sustainability Ethics and Governance
Published: 2019-01-01
From feed: (TITLE(ai PRE/3 sustainability))

written by James Canton Published by Csr Sustainability Ethics and Governance

Source

Douglas Fisher
IEEE Intelligent Systems
Published: 2016-07-01
From feed: (TITLE(ai PRE/3 sustainability))

This column surveys selected papers in computational sustainability from the 30th AAAI Conference on Artificial Intelligence.

Source

Douglas Fisher
IEEE Intelligent Systems
Published: 2012-07-01
From feed: (TITLE(ai PRE/3 sustainability))

This article intends, at a minimum, to give the briefest glimpse of the papers from the 2011 AAAI Computational Sustainability track, so that interested readers could follow up. Collectively, however, the contributions include representations of much that it might hope to see in an environmentally minded cognitive agent, with competencies in sensing and observation, knowledge-based reasoning, decision-making, and actuation. The creation of such agents could be an exciting and important ambition, with attention paid to agent communication as well. In the near term, however, AI will offer important, albeit specialized, cognitive tools for decision-makers. It is critical that these tools are adopted, adapted, and used by humans. Thus, it is vital that as part of our evaluation methodologies, it move beyond questions concerning technical optimality, and that it work with social, behavioral, and economic scientists to understand the social contexts in which our tools are used (or not)!

Source

Mario Miozza, Daniele Mascia, Angelo Paletta
Technovation.
Published: 2026-04-02
From feed: (TITLE(ai PRE/3 sustainability))

In today's rapidly evolving business landscape, organizations across industries are increasingly seeking to capitalize on the opportunities offered by digital transformation, particularly through Artificial Intelligence (AI). Yet, many organizations struggle to fully harness the potential of these emerging technologies. This study contributes to the ongoing discourse on the organizational factors that drive effective AI adoption, focusing on how organizational readiness influences AI adoption and performance. Specifically, we explore the relationship between Strategic and Cognitive readiness and AI adoption, examining their roles in advancing Sustainability and Economic outcomes. Using data from 511 employees in the pharmaceutical industry and employing Structural Equation Modeling, our analysis reveals that (i) strategic and cognitive readiness positively influence AI adoption; (ii) AI adoption mediates the positive relation between strategic and cognitive readiness and SDG performance; (iii) SDGs attainment mediates the relationship between AI adoption and economic performance; (iv) SDGs performance enhances economic performance; and that (v) this relationship is more pronounced in smaller organizations. Our findings contribute to the ongoing research on the organizational determinants of effective AI adoption and highlight the critical role of this technology in supporting both SDGs achievement and economic viability. • Organizational readiness for digital innovation leads to enhanced AI adoption levels. • AI adoption leads to enhanced Economic Performance, mediated by SDGs attainment. • SDGs attainment leads to enhanced Economic Performance. • Small Organizational Size moderates the effect between SDGs and Economic outcomes.

PDF | Source | Open access

Paul Hayes, Noël Fitzpatrick
SN Social Sciences.
Published: 2026-03-23
From feed: (TITLE(ai PRE/3 sustainability))

AI for good, and AI for sustainability projects, are being developed by often well-meaning innovators across the world, intending to support initiatives in sustainable development. Some such projects have been positioned within the framework of the Sustainable Development Goals. In this paper, we critically engage with this phenomenon using a virtue-based Ricoeurian narrative philosophy. Through conceptual analysis and normative argumentation, we make a theoretical contribution to scholarship on this topic. We argue that SDGs could be regarded as internationally agreed high-order end-norms that crystallise values considered to constitute the good life. We argue for exercising caution in applying SDGs as end-norms to AI projects as they are of a high order, are not directly action guiding, and threaten to sediment presentist dominant values. We argue for narrative hospitality between AI innovators and community stakeholders to guide reflection on specifying the SDGs to more situated contexts and to support the development of AI systems that may be more contextually appropriate, and more capable of supporting plural visions of the good life. Such narrative hospitality represents accommodation between parties, but refusal or resistance to engagement with AI innovators can be justified when community interests are not respected, and actors within AI industries can also be morally obliged to resist or refuse development or deployment of systems for contextually inappropriate environments that would be incompatible with a critical reading of the SDGs formed in narrative exchange with the other.

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Babak Jalalzadeh Fard, Sadid Hasan, Jesse E. Bell
Sustainability Switzerland.
Published: 2026-03-03
From feed: (TITLE(ai PRE/3 sustainability))

Advances in artificial intelligence (AI), particularly agentic AI, have created opportunities to enhance global sustainability by improving the efficiency and accuracy of environmental monitoring and response systems. Agentic AIs autonomously plan and execute towards specific goals with minimal or no human intervention; however, accessing environmental data is challenging and requires expertise due to inherent fragmentation and the diversity of data formats. The Model Context Protocol (MCP) is an open standard that allows AI systems to securely access and interact with diverse software tools and data sources through unified interfaces, reducing the need for custom integrations while enabling more accurate, context-aware assistance. This study introduces WeatherInfo_MCP, an interface that provides the required expertise for AI agents to access National Weather Service (NWS) data. Built on a service-oriented architecture, the system uses a centralized engine to handle robust geocoding and data extraction while providing AI agents with simple, independent tools to retrieve weather data from the NWS API. The system was validated through 14 unit tests and 23 comprehensive protocol compliance tests against the MCP 2025-06-18 specification, achieving a 100% pass rate across all categories, demonstrating its reliability when working with AI agents. We also successfully tested our model alongside a memory MCP to showcase its performance in a multi-MCP environment. While in its earliest version, WeatherInfo_MCP connects to the NWS API, its modular design and compliance with software development and MCP standards facilitate immediate expansion to additional environmental data and tools. WeatherInfo_MCP is released as an open-source tool to support the sustainable development community, enabling broad adoption of AI agents for environmental use cases.

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Arun Agrawal, Pratibhadevi Tapashetti, Shagufta Parween, Yalla Hari Krishna, Srinivasan. Nagaraj, K. B. Glory, D. Mishra, Deepak Gupta
Role of AI in Sustainable Supply Chain Management
Published: 2026-02-27
From feed: (TITLE(ai PRE/3 sustainability))

AI is emerging as a key force that roams the sustainability of supply chains. It assists the companies to minimize waste, make better use of resources as well as enhance performance. The AI tools help in real-time tracking, demand forecasting, optimizing routes, reducing energy, and responsible sourcing. These capabilities enable the businesses to be aware of risks during the first stage and also to take decisions that safeguard the environment and economy. AI also increases the transparency since it provides a clear picture of the supplier's activities and the carbon emissions. As sustainability in operations of industries is sought, AI offers solutions that are practical and make supply chains stronger. This paper describes the way in which AI enhances sustainability, brings out major applications, and gives a discussion on how organisations can pursue these technologies in a modest and affordable approach.

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Ahmad Subhi Salem Mufleh, Aashir Waleed, Sarah Mohammed Altuwayjiri, Loubna Hussain Rashid Alajmi, Mohamed Ibrahim Alsaid Hassan, Anwer Mustafa Hilal
Social Sciences and Humanities Open.
Published: 2026-02-23
From feed: (TITLE(ai PRE/3 sustainability))

This study examines the role of AI in addressing labor-market challenges, fostering occupational diversification, and advancing workforce development in Saudi Arabia within the framework of the Sustainable Development Goals (SDGs). The critical analysis is presented through a structured narrative that highlights AI's versatility in analyzing complex patterns, optimizing resources, and creating innovative job opportunities. It further underscores how AI can inspire youth to pursue emerging global trends rather than rely solely on traditional career pathways. Several AI models and applications are explored, including Generative AI, Computer Vision, Natural Language Processing (NLP), Big Data Analytics, Large Language Models (LLM), Predictive Analytics, Forecasting, and Optimization. These tools can help address workforce challenges in Saudi Arabia, including gender inequality, skills mismatches, occupational diversification, and the demands of a growing youth population. The integration of AI across sectors—from smart infrastructure and renewable energy optimization to healthcare innovations, robotic surgeries, and e-commerce—demonstrates its broad transformative potential. As a leading country in the Muslim world, Saudi Arabia can enhance its economic strength by leveraging AI in alignment with the Sdgs and visaion 2030, with a particular focus on empowering its young workforce to play an active role in the global economy. This study summarizes the Ethical, Legal, and Social Implications (ELSI) of AI Adoption, along with a mapping model of AI-labor market challenges. The study concludes by presenting a policy roadmap for future directions and outlining the limitations of the proposed framework. • This narrative review examines AI’s role in workforce development and job diversification in Saudi Arabia, aligned with SDGs. • It reviews AI models such as Generative AI, NLP, Computer Vision, and Predictive Analytics to address inequality, youth jobs, skills gaps. • A policy roadmap integrates ethical, legal, and social implications to guide AI labor reforms and empower Saudi youth.

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Abderrahim Laachach, Nora Sadiku‐Dushi
Business Strategy and the Environment
Published: 2026-02-11
From feed: (TITLE(ai PRE/3 sustainability))

ABSTRACT This study aims to elucidate the underlying psychological mechanisms by which generative artificial intelligence (GenAI) usage influences eco‐conscious behavior. Specifically, it investigates the mediating roles of environmental attitudes, perceived environmental impact, climate change anxiety (CCA), and environmental awareness, while examining the moderating effect of climate change intentions. Through this comprehensive framework, the research advances understanding of how cognitive, emotional, and motivational processes interact to shape sustainable behavior in digital contexts. This research utilized a quantitative methodology, surveying 474 participants, including Moroccan nationals and international visitors present in Morocco through online and offline methods. Partial least squares structural equation modeling (PLS‐SEM) was employed to rigorously test mediation and moderation relationships between GenAI usage intensity and eco‐conscious behavior across multiple psychological constructs. The findings reveal that GenAI usage intensity does not directly predict eco‐conscious behavior. Instead, environmental attitudes, perceived environmental impact, CCA, and environmental awareness fully mediate this relationship. Crucially, intentions to tackle climate change significantly amplify these indirect effects, emphasizing motivation as a key driver in converting AI engagement into sustainable behavioral change within the digital environmental context. The present paper highlights how companies can embed sustainability in GenAI by integrating real‐time carbon footprint feedback and motivational cues into AI systems, driving eco‐conscious user behavior. Coupled with strong governance and regulatory compliance, this approach mitigates AI's environmental impact while fostering responsible innovation. Aligning AI development with sustainability not only reduces emissions but also enhances competitive advantage and brand reputation, enabling firms to lead in sustainable digital transformation and support global climate goals.

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Frieder Bögner
Philosophy and Technology.
Published: 2026-01-30
From feed: (TITLE(ai PRE/3 sustainability))

Abstract Some challenges humans are facing do not apply to individuals alone and may not be solved individually. Sometimes, technology seems to provide relevant and promising strategies of problem solving for societal challenges. Choosing technological means and strategies to address encompassing challenges while alternative approaches would also be optional is sometimes criticised as an attitude of technosolutionism . Especially with emergent technologies like AI, this is an attitude common in technologically advanced societies that refers to AI technologies being proposed as solution strategies to societal challenges. It is often the case that instances of the critique need refinement as this would be of help to structuring debates, especially for discussions on the implementation of AI for sustainability issues. In this conceptual article, selected essential features of the attitude of technosolutionism as well as two example cases are discussed. The main goal is to distinguish existing variants of the objection of technosolutionism and to then propose a novel model of this objection, the lock-and-key model . According to this reading, it is a tendency of technology to crowd out other options in discourses on solution strategies due to its apparent seamless introduction. This will provide a new and productive understanding of the critique. Finally, the case of AI systems applied to issues of sustainability is discussed in the light of this model since in this area solutions are most urgent, the promises attached to technological solutions are wide-ranging and a novel conceptual tool is needed to promote reflected debates.

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Chamaipon Ratanacharoenchai, Konpapha Jantapoon
Sustainable Futures.
Published: 2026-01-23
From feed: (TITLE(ai PRE/3 sustainability))

This study investigates the complex relationships between artificial intelligence (AI) capabilities, supply chain resilience, and sustainability performance in small and medium enterprises (SMEs) within Thailand's manufacturing sector. Extending Dynamic Capabilities Theory (DCT) to AI-enabled organizational contexts, this research conceptualizes AI as a capability enhancer that transforms traditional sensing, seizing, and reconfiguring processes through algorithmic intelligence, rather than constituting an independent form of algorithmic dynamic capability. Drawing on Dynamic Capabilities Theory and the Resource-Based View, we surveyed 327 manufacturing SMEs implementing AI technologies to examine how AI-enabled sensing, seizing, and reconfiguring capabilities influence organizational outcomes. Using partial least squares structural equation modeling (PLS-SEM) with SmartPLS 4.0 software and 5000 bootstrap samples, our findings reveal that AI capabilities are significantly associated with supply chain resilience (R² = 0.542) and positively related to environmental sustainability (β = 0.426, p < .001), while showing no significant direct association with social sustainability (β = 0.073, p = .276). Critically, supply chain resilience fully mediates the relationship between AI and social sustainability, with variance accounted for (VAF) of 69.7%, indicating that SMEs must first achieve operational stability before realizing social benefits. Multi-group analysis reveals significant moderation effects, with firm size, implementation maturity, and industry context shaping how AI capabilities translate into performance outcomes. For SME managers, findings emphasize strengthening AI-driven seizing processes for data-based decision making through investment in real-time analytics platforms. For policymakers, results support designing phased AI maturity programs with staged incentive structures for small manufacturers. For consultants, the study provides a foundation for developing AI-based resilience assessment frameworks and implementation roadmaps.

Source | Open access