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Journal articles on the topic 'Green Artificial Intelligence'

Author: Grafiati

Published: 7 June 2025

Last updated: 16 July 2025

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1

Debrah, Caleb, Albert P. C. Chan, and Amos Darko. "Artificial intelligence in green building." Automation in Construction 137 (May 2022): 104192. http://dx.doi.org/10.1016/j.autcon.2022.104192.

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2

Bhatnagar, Apoorva, and Megha Sharma. "Artificial Intelligence and Consumer’s Perception: A Research on Environmentally Conscious Consumer." Journal of Metaverse 4, no. 2 (2024): 105–15. http://dx.doi.org/10.57019/jmv.1488022.

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The purpose of this study is to explore the limited exploration of the simultaneous influence of beneficial artificial intelligence, destructive artificial intelligence, and risky artificial intelligence on green purchase intention and green purchase behaviour using the Technology Acceptance Model (TAM) and Innovation Resistance Theory (IRT). Further, it also checks the impact of green purchase intention on green purchase behaviour. Data was collected using a well-structured questionnaire from 124 consumers through online mode and analyzed using Confirmatory Factor Analysis (CFA) for reliabili

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ABD EL- MAKSOUD, NESMA, and Eman Ahmed. "Artificial Intelligence Applications in Green Architecture." Fayoum University Journal of Engineering 7, no. 2 (2024): 317–37. http://dx.doi.org/10.21608/fuje.2024.345049.

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Alzoubi, Yehia Ibrahim, and Alok Mishra. "Green artificial intelligence initiatives: Potentials and challenges." Journal of Cleaner Production 468 (August 2024): 143090. http://dx.doi.org/10.1016/j.jclepro.2024.143090.

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5

Swaminathan, Ramanathan. "The Emergent Artificial Intelligence of Green Spaces." Asiascape: Digital Asia 2, no. 3 (2015): 238–78. http://dx.doi.org/10.1163/22142312-12340032.

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The paper focuses on the digital logic that informs debates on urban ecology and green spaces in Asia. First, the paper builds a case regarding how pervasive digitalization is embedding everyday activities with artificial intelligence. This process, the paper argues, reconfigures existing social relationships of power and creates new modes of articulation, engagement, contestation, and negotiation. Second, the paper specifically looks at how this process informs material productions of space, spatiality, and territoriality of urban ecology. As case studies, the paper maps the narratives and di

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Dr., Anil Kumar Pandey. "DEVELOPMENT AND DEPLOYMENT OF GREEN ARTIFICIAL INTELLIGENCE." INTERNATIONAL JOURNAL OF MATHEMATICS AND COMPUTER RESEARCH 11, no. 04 (2023): 3328–32. https://doi.org/10.5281/zenodo.7836589.

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Green AI is an emerging field that aims to develop and deploy environmentally friendly and sustainable artificial intelligence (AI) systems. The potential benefits of Green AI to society are numerous, as it can help reduce the environmental impact of various sectors while improving their efficiency and effectiveness. This includes optimizing the operation of smart grids, reducing energy consumption in buildings, improving crop yields in agriculture, optimizing public transportation, monitoring and analyzing environmental data, managing waste, optimizing manufacturing processes, and modeling th

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Lu, Guanyan, and Bingxiang Li. "Artificial Intelligence and Green Collaborative Innovation: An Empirical Investigation Based on a High-Dimensional Fixed Effects Model." Sustainability 17, no. 9 (2025): 4141. https://doi.org/10.3390/su17094141.

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This study focuses on the intrinsic mechanisms and sustainable value of artificial intelligence (AI)-driven green collaborative innovation in enterprises amid the global green low-carbon transition, revealing new pathways for digital technology-enabled green development. Based on the data of China’s A-share listed companies jointly applying for green patents with other entities from 2010 to 2023, this study used a high-dimensional fixed effect model to empirically find that artificial intelligence significantly promotes green collaborative innovation. This promoting effect proved more pronounc

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Du, Jiaxing, Han Cai, and Xiu Jin. "Exploring the Association Between Artificial Intelligence Management and Green Innovation: Expanding the Research Field for Sustainable Outcomes." Sustainability 16, no. 21 (2024): 9315. http://dx.doi.org/10.3390/su16219315.

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Green innovation is essential for achieving sustainable development goals (SDGs), particularly SDG 9 (Industry, Innovation, and Infrastructure) and SDG 12 (Responsible Consumption and Production), as it fosters environmental and social benefits while also creating new economic opportunities. Despite previous studies actively conducting empirical analyses on green innovation, research on guiding the green innovation process through artificial intelligence remains scarce. This study aims to explore key variables that affect green innovation, thereby promoting the sustainable development of organ

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Li, Hanna, and Yu Chen. "Does Artificial Intelligence Promote Firms’ Green Technological Innovation?" Sustainability 17, no. 11 (2025): 4900. https://doi.org/10.3390/su17114900.

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Green technological innovation represents one of the critical driving forces for addressing environmental issues and advancing the sustainable development process. As a key driver of the new round of technological transformation, artificial intelligence is bound to exert significant impacts on firms’ green technological innovation. In this study, green technology innovation is divided into clean production and pollution control technology innovation according to the production link. A double fixed-effects model was used to test the impact of AI using data from Chinese listed companies from 200

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10

Ambreen, Uzma, and Khawar Sultana. "Artificial Intelligence, Environmental sustainability, Eco-Friendly products, Islamic Values and green Purchase Intension." Journal of Excellence in Management Sciences 3, no. 1 (2024): 191–210. http://dx.doi.org/10.69565/jems.v3i1.182.

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Artificial intelligence (AI) is used in several fields i.e. medicine, human resource, physics, etc; however, its use in the sustainable environment is limited. This paper discusses the use of artificial intelligence to increase the green purchase intention (GPI) of the customers in Pakistan with the moderating effect of Islamic values. The data for this research was gathered from existing articles and Quran verses. The results of the study show that the green purchase intention of the customers can be affected through artificial intelligence and Islamic values. Moreover, it also encourages the

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Chen, Zheng, and Yu He. "Artificial Intelligence and Environmental Protection of Buildings." Problemy Ekorozwoju 18, no. 2 (2023): 254–62. http://dx.doi.org/10.35784/preko.4039.

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Global environmental pollution has an extremely negative impact on the population of the planet and threatens the future of mankind. One of the main sources of waste and toxic emissions into the atmosphere is the construction sector. It is necessary to find ways to minimize the damage caused to nature. Currently, artificial intelligence technologies are among the most promising ways to improve the environment. Automatic control systems solve a number of problems related to reducing costs and resources, full use of renewable energy sources, improving the safety of energy systems, and many other

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12

Chen, Mingyue, Shuting Wang, and Xiaowen Wang. "How Does Artificial Intelligence Impact Green Development? Evidence from China." Sustainability 16, no. 3 (2024): 1260. http://dx.doi.org/10.3390/su16031260.

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Artificial intelligence not only changes the production methods of traditional industries but also provides an important opportunity to decouple industrial development from environmental degradation and promote green economic growth. In order to further explore the green value of AI, this paper constructs an indicator of industrial robot penetration at the regional level, based on the idea of Bartik’s instrumental variable, and measures green development efficiency using the improved Super-SBM model. Based on a comprehensive explanation of the influence mechanism, a spatial measurement model a

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Heredia Yzquierdo, F. Javier. "Artificial Intelligence and the Green Transition for 2030." icade. Revista de la Facultad de Derecho, no. 112 (December 13, 2021): 1–17. http://dx.doi.org/10.14422/icade.i112.y2021.007.

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The United Nations have developed the 2030 Agenda for Sustainable Development as a global agreement aiming to eradicate extreme poverty, fight inequality and injustice and leave no one behind. Agreed by world leaders at the UN in 2015, the 17 Sustainable Development Goals (SDGs) succeed the well-known Millennium Development Goals (MDGs). Parts of the Goals directly address environmental issues. Other do indirectly. In response to this growing awareness, the European Union has enacted a number of policies targeting environmental issues, including lowering carbon emissions and water conservation

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14

Zhou, Chaobo, Haikuo Zhang, Jinhuika Ying, Shouchao He, Chong Zhang, and Jiale Yan. "Artificial intelligence and green transformation of manufacturing enterprises." International Review of Financial Analysis 104 (August 2025): 104330. https://doi.org/10.1016/j.irfa.2025.104330.

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15

Li, Huanyu, Hao Wu, and Jian Rao. "Impact of artificial intelligence on corporate green transformation." Finance Research Letters 80 (June 2025): 107427. https://doi.org/10.1016/j.frl.2025.107427.

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16

Ms. M. Valarmathi, Dr. N. Kowsalya, Ms. S. Samundeeswari, Mr. M. Baskar, Ms. R. Vimala Devi, and Ms. N. Kokila. "Green Energy Solutions Advancing Sustainability through Artificial Intelligence." International Research Journal on Advanced Engineering and Management (IRJAEM) 3, no. 02 (2025): 355–59. https://doi.org/10.47392/irjaem.2025.0056.

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The urgent need to conflict climate change and reduce reliance on fossil fuels has driven the universal evolution to regenerative energy. However, the efficient integration and management of regenerative energy origin such as solar, hydropower wind, pose essential challenges, including intermittency, energy storage, and grid optimization. This paper presents an analysis in the impact of AI for the enhancement of renewable energy production. By adopting AI-Driven algorithms and methods such as predictive analysis, Calibrating energy generation, tracking and ensuring operations of smart grid, de

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qizi, Fozila Sherzodova Bexzod. "The Role of Artificial Intelligence in Saving the Earth." American Journal of Applied Science and Technology 5, no. 5 (2025): 170–72. https://doi.org/10.37547/ajast/volume05issue05-34.

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This research paper explores how Artificial Intelligence (AI) and data analytics are transforming the landscape of environmental sustainability. It discusses the growing role of AI in climate prediction, resource management, pollution monitoring, and conservation efforts. Through case studies and scholarly references, it examines the benefits, such as improved decision-making and efficiency, alongside challenges such as data bias, ethical concerns, and energy consumption. The study concludes by emphasizing future directions, including green AI, collaborative frameworks, and inclusive technolog

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18

Gao, Liqian, and Ziyang Liu. "Unraveling the Multifaceted Nexus of Artificial Intelligence Sports and User Willingness: A Focus on Technology Readiness, Perceived Usefulness, and Green Consciousness." Sustainability 15, no. 18 (2023): 13961. http://dx.doi.org/10.3390/su151813961.

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The adoption of artificial intelligence (AI) in the sports domain, known as AI Sports, has gained considerable attention, while the increasing importance of environmental sustainability further necessitates exploring green-conscious behaviors. This study aims to investigate the impacts of technology readiness, perceived usefulness, and green consciousness on users’ willingness to adopt AI Sports. Utilizing a cross-sectional survey and structural equation modeling, data from 670 valid questionnaires were analyzed. The results revealed that technology readiness directly influences users’ perceiv

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Jović-Bogdanović, Adriana, Milan Radosavljević, and Maja Anđelković. "Management of eco-hotel new product using artificial intelligence." International Review, no. 1-2 (2024): 79–87. http://dx.doi.org/10.5937/intrev2401079j.

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The modern economy rests on industrial development and production with the application of new technologies, knowledge and achievements in technique, technology, economy, ecology, organization, management, informatics and other branches of science and profession. Processes are being innovated in order to achieve products and services of higher quality and performance, in accordance with increasingly strict market requirements. Today, KETs (Key Enabling Technologies) are increasingly being used, which represent a narrower set ofsix KETs that enable innovations: micro and nanoelectronics, nanotec

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Brevini, Benedetta. "Inteligência Artificial, Soluções Artificiais - Colocando a emergência climática no centro dos desenvolvimentos de IA." Revista Mídia e Cotidiano 18, no. 2 (2024): 11–26. http://dx.doi.org/10.22409/rmc.v18i2.63056.

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Despite growing attention to the environmental costs of information communication technology systems, Artificial Intelligence gets principally heralded as the key technology to solve contemporary challenges, including the environmental crisis, which is one of the goals of sustainable development. But how green is Artificial Intelligence? This text considers the materiality of AI and attempts to go beyond mainstream hypes as it explores the multifaceted ways in which AI is impacting the climate emergency, thus impacting sustainable environmental development. It concludes by offering a set of so

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21

Chishti, Muhammad Zubair, Eyup Dogan, and Rima H. Binsaeed. "Can artificial intelligence and green finance affect economic cycles?" Technological Forecasting and Social Change 209 (December 2024): 123740. http://dx.doi.org/10.1016/j.techfore.2024.123740.

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22

Omar, Omar, and Omar Al Al-Boridi. "Artificial Intelligence-Enhanced Green Building Design for Environmental Sustainability." Fusion: Practice and Applications 17, no. 2 (2025): 197–210. http://dx.doi.org/10.54216/fpa.170215.

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Green buildings are those that use sustainable methods of construction to either maintain or improve the local quality of life. Decisions affecting a project's quality, safety, profitability, and timetable are made using Artificial Intelligence (AI) in Green Construction by analyzing data gathered from monitoring the construction site and using predictive analytics. For instance, increased accuracy in weather predictions might lead to more production, less waste, lower costs, and less greenhouse gas emissions. Green building construction is a significant source of carbon dioxide released throu

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23

Feng, Zhang, Minyue Ge, and Qian Meng. "Enhancing Energy Efficiency in Green Buildings through Artificial Intelligence." Frontiers in Science and Engineering 4, no. 8 (2024): 21–30. http://dx.doi.org/10.54691/py2h2y60.

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Artificial Intelligence (AI) is poised to revolutionize the architectural design and energy management of green buildings, offering significant advancements in sustainability and efficiency. This paper explores the transformative impact of AI on improving energy efficiency and reducing carbon emissions in commercial buildings. By leveraging AI algorithms, architects can optimize building performance through advanced environmental analysis, automation of repetitive tasks, and real-time data-driven decision-making. AI facilitates precise energy consumption forecasting and integration of renewabl

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24

Maysaa H. Abdulameer. "Artificial Intelligence-Enhanced Green Building Design for Environmental Sustainability." Journal of Information Systems Engineering and Management 10, no. 8s (2025): 360–76. https://doi.org/10.52783/jisem.v10i8s.1052.

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Green buildings are those that use sustainable methods of construction to either maintain or improve the local quality of life. Decisions affecting a project's quality, safety, profitability, and timetable are made using Artificial Intelligence (AI) in Green Construction by analyzing data gathered from monitoring the construction site and using predictive analytics. For instance, increased accuracy in weather predictions might lead to more production, less waste, lower costs, and less greenhouse gas emissions. Green building construction is a significant source of carbon dioxide released throu

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25

Tsang, S. W., and C. Y. Jim. "Applying artificial intelligence modeling to optimize green roof irrigation." Energy and Buildings 127 (September 2016): 360–69. http://dx.doi.org/10.1016/j.enbuild.2016.06.005.

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26

Kumar Gupta, Nitesh, Deepak Kumar Namdeo, Dipti Dubey, and Dr Subodhini Gupta. "Artificial Intelligence, Robotics And Its Applications In Green Libraries." Journal of Survey in Fisheries Sciences 10, no. 2 (2023): 1144–52. http://dx.doi.org/10.53555//sfs.v10i2.1682.

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27

Zhang, Feng, Ge Minyue, and Meng Qian. "Enhancing Energy Efficiency in Green Buildings through Artificial Intelligence." Applied Science and Engineering Journal for Advanced Research 3, no. 5 (2024): 10–17. https://doi.org/10.5281/zenodo.13948759.

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Artificial Intelligence (AI) is poised to revolutionize the architectural design and energy management of green buildings, offering significant advancements in sustainability and efficiency. This paper explores the transformative impact of AI on improving energy efficiency and reducing carbon emissions in commercial buildings. By leveraging AI algorithms, architects can optimize building performance through advanced environmental analysis, automation of repetitive tasks, and real-time data-driven decision-making. AI facilitates precise energy consumption forecasting and integration of renewabl

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28

Xianfeng, HAN, LI Jiajia, and ZHU Chengliang. "Inclusive green growth effects of China’s artificial intelligence development." 资源科学 47, no. 6 (2025): 1278–94. https://doi.org/10.18402/resci.2025.06.09.

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29

Khairy, Hazem Ahmed, Mohamed Ahmed, Arwa Asiri, et al. "Catalyzing Green Work Engagement in Hotel Businesses: Leveraging Artificial Intelligence." Sustainability 16, no. 16 (2024): 7102. http://dx.doi.org/10.3390/su16167102.

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This study explores green work engagement in response to the global demand for sustainability in businesses and the shift toward green-oriented agendas. Specifically, this study aims to examine how green work engagement (GWE) is affected by artificial intelligence awareness (AIA) through job stress (JS) as a mediator. It also explores the moderating roles of technological self-efficacy (TSE) in the AIA→JS relationship and trust in leadership (TIL) in the GWE→JS relationship. A PLS-SEM analysis was conducted on 392 valid replies from full-time employees of five-star hotels in Egypt using WarpPL

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Daniel, Sameer. "Artificial Intelligence for Agroforestry: A Review." Journal of Scientific Research and Reports 31, no. 7 (2025): 668–77. https://doi.org/10.9734/jsrr/2025/v31i73286.

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This review explores about use of artificial intelligence techniques for agroforestry. Agroforestry is an intensive and interactive land usage strategy that maximises biotic and abiotic resources by deliberately combining trees and/or shrubs with crops and/or animals in temporal and spatial patterns on the same plot of land. Agroforestry is a self-sustaining, green and smart technology that will transform the future of Indian agriculture. AI-powered agroforestry plays a critical role in data collecting, processing, assessment, interpretation, knowledge acquisition, and solution provision to im

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Nguyen, Hoang Phuong, Cao Thao Uyen Nguyen, Thi Men Tran, Quoc Hai Dang, and Nguyen Dang Khoa Pham. "Artificial Intelligence and Machine Learning for Green Shipping: Navigating towards Sustainable Maritime Practices." JOIV : International Journal on Informatics Visualization 8, no. 1 (2024): 1. http://dx.doi.org/10.62527/joiv.8.1.2581.

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This paper aims to investigate the role that artificial intelligence (AI) plays in promoting sustainability in the marine industry. The report demonstrates the potential of AI-driven technology to improve vessel operations, decrease emissions, and promote environmental stewardship. This potential is shown by detailed examination of existing trends, problems, and possibilities. Several vital studies highlight the significance of policy interventions that encourage the use of artificial intelligence. These interventions include financial incentives, legal frameworks, and programs to increase cap

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32

Recio-Colmenares, Carolina Livier, Jean Flores-Gómez, Juan Pablo Morales Rivera, Hasbleidy Palacios Hinestroza, and Belkis Sulbarán-Rangel. "Green Materials for Water and Wastewater Treatment: Mechanisms and Artificial Intelligence." Processes 13, no. 2 (2025): 566. https://doi.org/10.3390/pr13020566.

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Green materials are emerging as sustainable alternatives in water and wastewater treatment. Due to their biodegradability, renewable origin and low toxicity characteristics, green materials are an alternative to conventional synthetic materials. Green materials include nanomaterials of natural origin, biopolymers and composites that optimize the adsorption and removal of contaminants. The applications of cellulose nanofibers, alginates, chitosan and lignin stand out, as well as functionalized hydrogels and aerogels for the removal of heavy metals, dyes and organic contaminants. The analysis of

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Srikantha J K, Dr. Ashoka K S, Shubha J K, and Punith kumar S R. "Artificial Intelligence in seed science." World Journal of Advanced Research and Reviews 27, no. 1 (2025): 852–66. https://doi.org/10.30574/wjarr.2025.27.1.2533.

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The accelerating global population growth, as highlighted by the United Nations Food and Agriculture Organization (UNFAO), intensifies the demand for sustainable food production, particularly in land-constrained regions like India. Despite the historical gains of the Green Revolution, modern Indian agriculture grapples with persistent challenges such as soil degradation, plant diseases, and diminishing crop yields—plant diseases alone account for nearly 40% of losses. In this context, Artificial Intelligence (AI) presents a transformative opportunity to address these issues through precision,

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Kurek, Jarosław, Artur Krupa, Izabella Antoniuk, et al. "Improved Drill State Recognition during Milling Process Using Artificial Intelligence." Sensors 23, no. 1 (2023): 448. http://dx.doi.org/10.3390/s23010448.

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In this article, an automated method for tool condition monitoring is presented. When producing items in large quantities, pointing out the exact time when the element needs to be exchanged is crucial. If performed too early, the operator gets rid of a good drill, also resulting in production downtime increase if this operation is repeated too often. On the other hand, continuing production with a worn tool might result in a poor-quality product and financial loss for the manufacturer. In the presented approach, drill wear is classified using three states representing decreasing quality: green

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POPESCU, Oana-Cătălina, Antonio-Valentin TACHE, and Alexandru-Ionuţ PETRIŞOR. "Connecting nature and technology: using artificial intelligence to plan greener cities." Present Environment and Sustainable Development 18, no. 2 (2024): 345–54. https://doi.org/10.47743/pesd2024182019.

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The field of spatial planning uses digital technologies as support for planning and decision-making activities. Recently, artificial intelligence technologies started to be used also in green infrastructure planning to allow better decisions and to find solutions to mitigate climate changes especially in urban areas. Optimization algorithms are used to plan green infrastructure at all scales, from the level of a tree to residential and other urban green and natural spaces. Therefore, the main objective of this study is to outline the necessity of implementing AI technology in urban areas to im

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Lukashina, Elena V., Alexandr V. Lukashin, and Valery I. Bezrukov. "The use of Artificial Intelligence technologies in building business architectures within the framework of the “green economy”." E3S Web of Conferences 458 (2023): 05025. http://dx.doi.org/10.1051/e3sconf/202345805025.

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The article discusses the forms of using artificial intelligence technologies in the study of business architectures in the context of the development of a “green” economy. The authors suggest using various business platforms for the development of a “green” economy. The article proves that with the help of artificial intelligence, it is possible, within the framework of sustainable development, to effectively involve business in solving problems of optimizing and greening economic processes in order to save energy resources and the environment.

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Ying, Ying, Xiaoyan Cui, and Shanyue Jin. "Artificial Intelligence and Green Total Factor Productivity: The Moderating Effect of Slack Resources." Systems 11, no. 7 (2023): 356. http://dx.doi.org/10.3390/systems11070356.

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With the emergence of the digital economy, digital technologies—such as artificial intelligence (AI)—have provided new possibilities for the green development of enterprises. Green total factor productivity is a key indicator of green sustainable development. While traditional total factor productivity does not consider the constraints of natural resources and the environment, green total factor productivity remedies this deficiency by incorporating environmental protection indicators, such as pollutant emissions, into the accounting system. To further clarify the relationship between AI techn

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Jiang, Xiangyu, Gu-Hong Lin, Jui-Chan Huang, I.-Hsiang Hu, and Yen-Chun Chiu. "Performance of Sustainable Development and Technological Innovation Based on Green Manufacturing Technology of Artificial Intelligence and Block Chain." Mathematical Problems in Engineering 2021 (April 3, 2021): 1–11. http://dx.doi.org/10.1155/2021/5527489.

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The powerful advanced manufacturing industry is the most powerful driving force for economic development and growth, and it is also the main source of environmental pollution. Artificial intelligence and blockchain technology are recognized as a breakthrough technology that can be widely used, changing the way the entire society and economy operate. The main constraints affecting the sustainable development of the manufacturing industry are ecological deterioration and resource shortage, but the development of artificial intelligence and blockchain technology provides new ideas for solving man

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Xu, Binjie. "Risk Assessment of Green Intelligent Building Based on Artificial Intelligence." Computational Intelligence and Neuroscience 2022 (September 15, 2022): 1–8. http://dx.doi.org/10.1155/2022/7584853.

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Green smart building is the development direction of future architecture. It is of great significance to carry out risk assessment. Fire risk is the key content of building risk, so this paper takes fire risk as the research object, with the help of artificial intelligence technology, to carry out the risk assessment research of green smart buildings. With the rapid development of the economy, urban fire risk factors are increasing, and the fire situation is becoming more and more serious. Building fire risk assessment is an important measure to effectively prevent and control urban building f

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Nafi'iyah, Nur, Endang Setyati, Yosi Kristian, and Retno Wardhani. "Recognizing the Types of Beans Using Artificial Intelligence." Register 9, no. 2 (2023): 134–43. http://dx.doi.org/10.26594/register.v9i2.3054.

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Many studies have previously addressed the recognition of plant leaf types. The process of identifying these leaf types involves a crucial feature extraction stage. Image feature extraction is pivotal for distinguishing the types of objects, thus demanding optimal feature analysis for accurate leaf type determination. Prior research, which employed the CNN method, faced challenges in effectively distinguishing between long bean and green bean leaves when identifying bean leaves. Therefore, there is a need to conduct optimal feature analysis to correctly classify bean leaves. In our research, w

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Li, Lupeng, and Bo Chen. "Analysis of the Current Status, Challenges, and Countermeasures of Supply Chain Management in Chinese Enterprises." Economics & Business Management 2, no. 1 (2025): 156. https://doi.org/10.63313/ebm.2015.

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This article analyses the current state and challenges of supply chain man-age-ment in China, highlighting the insufficient penetration of digital technolo-gy, the need to improve safety and sustainability, the difficulty of internal col-labo-ration within enterprises, and the opportunities and challenges posed by the application of artificial intelligence. Data heterogeneity, information securi-ty risks, partner credit shortages and technological management lag are hinder-ing supply chain efficiency. While artificial intelligence is driving transfor-mation, it is also disrupting traditional m

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Ghahremani Nahr, Javid, Hamed Nozari, and Mohammad Ebrahim Sadeghi. "Green supply chain based on artificial intelligence of things (AIoT)." International Journal of Innovation in Management, Economics and Social Sciences 1, no. 2 (2021): 56–63. http://dx.doi.org/10.52547/ijimes.1.2.56.

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Anditya, Ariesta Wibisono, and Duan Yan Ran. "The Emerging Artificial Intelligence and the Green Victimology in Indonesia." Southeast Asian Journal of Victimology 2, no. 1 (2024): 1. http://dx.doi.org/10.51825/sajv.v2i1.25448.

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Qian, Kai. "Optimization Algorithm for Green Environment Design Based on Artificial Intelligence." Scalable Computing: Practice and Experience 25, no. 5 (2024): 3547–53. http://dx.doi.org/10.12694/scpe.v25i5.3123.

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In order to build energy-efficient commercial buildings in bustling urban centers and utilize passive means suchas natural ventilation and natural lighting as much as possible to improve indoor environmental quality, the author proposes agreen environment design optimization algorithm based on artificial intelligence. Taking Building A as an example, simulationtechnology was used to optimize the performance of the enclosure structure, anti condensation in the glass atrium, and naturalventilation during the transition season. The sunlight and shadow simulation software BSAT was used to simulate

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Petković, Miloš. "How Does Artificial Intelligence Influence on Company’s Green Intellectual Capital?" Ecologica 30, no. 112 (2023): 541–48. http://dx.doi.org/10.18485/ecologica.2023.30.112.5.

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Sodiq Fowosere, Courage Obofoni Esechie, Sarah Namboozo, and Friday Anwansedo. "The Role of Artificial Intelligence in Green Supply Chain Management." International Journal of Latest Technology in Engineering Management & Applied Science 14, no. 2 (2025): 325–31. https://doi.org/10.51583/ijltemas.2025.14020033.

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Abstract: As environmental concerns continue to grow, industries are compelled to implement sustainable supply chain processes. Green supply chain management (GSCM) has become a key strategy for reducing the negative effects of supply chains on the environment. It includes anything from energy-efficient logistics to environmentally friendly product design. Simultaneously, artificial intelligence (AI) is transforming supply chain management through improved decision-making, optimization, and efficiency capabilities. Businesses have a great chance to achieve sustainability objectives while prese

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Niftiyev, Ibrahim. "Artificial intelligence in green management and sustainability: A bibliometric survey." E3S Web of Conferences 608 (2025): 05019. https://doi.org/10.1051/e3sconf/202560805019.

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The intersection of artificial intelligence (AI), green management and sustainability provides a unique opportunity to address critical environmental issues with cutting-edge technologies. The purpose of this study is to examine the developments and trends in the literature using a bibliometric analysis method. The data were collected between 1991 (January 1) and 2024 (April 1) in the Scopus database. The results show that the focus of AI and sustainability research has shifted from, for example, water management to broader environmental concerns and then to new analytical tools such as deep l

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Nozari, Hamed. "Green Supply Chain Management based on Artificial Intelligence of Everything." Journal of Economics and Management 46 (2024): 171–88. http://dx.doi.org/10.22367/jem.2024.46.07.

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Aim/purpose – This research aims to design an analytical framework to investigate the dimensions, factors, and key indicators affecting the green supply chain based on the innovative technology of Artificial Intelligence of Everything (AIoE). Understanding the cause-and-effect relationships of all actors in this smart and sustainable system is also one of the critical goals of this research. Also, examining the key features of AIoE tech- nology as a new hybrid technology is one of this research’s most essential features. Design/methodology/approach – This research has tried to extract and refi

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Zhang, Yuanhe, and Chaobo Zhou. "Effect of Artificial Intelligence on Chinese Urban Green Total Factor Productivity." Land 14, no. 3 (2025): 660. https://doi.org/10.3390/land14030660.

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The manner of achieving high-quality economic development in China through artificial intelligence (AI) has become a focus of academic attention. On the basis of panel data of prefecture-level cities in China from 2010 to 2021, this research utilizes the exogenous impact of the implementation of the National New Generation Artificial Intelligence Innovation and Development Pilot Zone (AIPZ) to explore the causal effect between AI and green total factor productivity (GTFP). The results are as follows: (1) AI has a significant enhancement effect on urban GTFP. After using a series of robustness

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Jaca Madariaga, Maite, ENARA ZARRABEITIA BILBAO, ROSA MARIA RIO BELVER, and MARIE FRANCINE MOENS. "GREEN HYDROGEN: A SOCIAL MEDIA INTELLIGENCE ANALYSIS." DYNA 99, no. 3 (2024): 250–55. http://dx.doi.org/10.6036/11069.

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Given the current state of climate change and the fight against it, green hydrogen has the potential to be the energy vector of the future within a context of decarbonisation. In this way, fossil fuels might be replaced by green hydrogen in those sectors that are most difficult to decarbonise. Therefore, in recent years it is becoming more and more common to hear about green hydrogen in society and the great advantages of using it, although, it also has some disadvantages, mostly related to higher costs and higher energy consumption. With this in mind, the purpose of this paper is to use socia

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