The carbon impact of artificial intelligence

Strategies of Automated Machine Learning for Energy Sustainability in Green Artificial Intelligence

Castellanos–Nieves, D., & García-Forte, L. (2024) · Applied Sciences · doi:10.3390/app14146196 · Cited by 30

Ethical Artificial Intelligence in Chemical Research and Development: A Dual Advantage for Sustainability

Hermann, E., Hermann, G., & Tremblay, J. C. (2021) · Science and Engineering Ethics · doi:10.1007/s11948-021-00325-6 · Cited by 33

Data smells in public datasets

Shome, A., Cruz, L., & Arie Van Deursen, A. (2022) · IEEE/ACM International Conference on AI Engineering – Software Engineering for AI (CAIN) · doi:10.1145/3522664.3528621 · Cited by 27

Weak self-supervised learning for seizure forecasting: a feasibility study

Yang, Y., Truong, N. D., Eshraghian, J. K., Nikpour, A., & Kavehei, O. (2022) · Royal Society Open Science · doi:10.1098/rsos.220374 · Cited by 23

Role of Artificial Intelligence in Minimizing Carbon Footprint: A Systematic Review of Recent Insights

Arya, A. K., Bachheti, A., Bachheti, R. K., Singh, M. M., & Chandel, A. K. (2024) · Green energy and technology · doi:10.1007/978-3-031-51601-6_14 · Cited by 28

Streamflow Prediction with Time-Lag-Informed Random Forest and Its Performance Compared to SWAT in Diverse Catchments

Moges, D. M., Virro, H., Kmoch, A., Cibin, R., Rohith, R. A. N., Martínez‐Salvador, A., García, C. C., & Uuemaa, E. (2024) · Water · doi:10.3390/w16192805 · Cited by 22

Synergy of Patent and Open-Source-Driven Sustainable Climate Governance under Green AI: A Case Study of TinyML

Li, T., Luo, J., Liang, K., Yi, C., & Ma, L. (2023) · Sustainability · doi:10.3390/su151813779 · Cited by 23

In‐Sensor Passive Speech Classification with Phononic Metamaterials

Dubček, T., Moreno‐Garcia, D., Haag, T., Omidvar, P., Thomsen, H. R., Becker, T. S., Gebraad, L., Bärlocher, C., Андерссон, Ф., Huber, S. D., et al. (2024) · Advanced Functional Materials · doi:10.1002/adfm.202311877 · Cited by 33

Does artificial intelligence (AI) reduce ecological footprint? The role of globalization

Wang, Q., Sun, T., & Li, R. (2023) · Environmental Science and Pollution Research · doi:10.1007/s11356-023-31076-5 · Cited by 29

Digital transformation, intelligent device utilization, and carbon emission reduction

Chu, Y., Cheng, M., Zhao, X., & Zhou, D. (2023) · Journal of Digital Economy · doi:10.1016/j.jdec.2023.12.004 · Cited by 22

Quantum algorithms for scientific computing

Au-Yeung, R., Camino, B., Rathore, O., & Kendon, V. M. (2024) · Reports on Progress in Physics · doi:10.1088/1361-6633/ad85f0 · Cited by 37

AI carbon footprint in China sets to double post-2030 carbon peaking

Chen, Z., Xiong, Q., Duan, J., Ma, J., Chen, Z., & Guo, S. (2025) · Energy Economics · doi:10.1016/j.eneco.2025.108880 · Cited by 22

The Impact of Data Analytics Based on Internet of Things, Edge Computing, and Artificial Intelligence on Energy Efficiency in Smart Environment

Rojek, I., Prokopowicz, P., Piechowiak, M., Kotlarz, P., Náprstková, N., & Mikołajewski, D. (2025) · Applied Sciences · doi:10.3390/app16010225 · Cited by 35

Reviewing the socio-technical dynamics of AI, data centers and digitalization on energy and the environment

Kim, J., Kim, J., Sovacool, B. K., Griffiths, S., Bazilian, M., Creutzig, F., Rio, D. D. F. D., Agarwala, M., Choi, M., & Debnath, R. (2026) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2026.116861 · Cited by 25

Carburacy: Summarization Models Tuning and Comparison in Eco-Sustainable Regimes with a Novel Carbon-Aware Accuracy

Moro, G., Ragazzi, L., & Valgimigli, L. (2023) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v37i12.26686 · Cited by 27

AI, Behavioural Science, and Consumer Welfare

Mills, S., Costa, S., & Sunstein, C. R. (2023) · Journal of Consumer Policy · doi:10.1007/s10603-023-09547-6 · Cited by 35

Seeing the random forest through the decision trees. Supporting learning health systems from histopathology with machine learning models: Challenges and opportunities

González, R., Saha, A., Campbell, C. J. V., Nejat, P., Lokker, C., & Norgan, A. P. (2023) · Journal of Pathology Informatics · doi:10.1016/j.jpi.2023.100347 · Cited by 24

Making AI Ethical by Design: The UNESCO Perspective

Ramos, G., Squicciarini, M., & Lamm, E. (2024) · Computer · doi:10.1109/mc.2023.3325949 · Cited by 24

Behind the good of digital tools for occupational safety and health: a scoping review of ethical issues surrounding the use of the internet of things

Bouchikhi, M. E., Weerts, S., & Clavien, C. (2024) · Frontiers in Public Health · doi:10.3389/fpubh.2024.1468646 · Cited by 23

Carbon capture, utilization and storage in buildings: Analysis of performance, social acceptance, policy measures, and the role of artificial intelligence

Elaouzy, Y., & Zaabout, A. (2025) · Building and Environment · doi:10.1016/j.buildenv.2025.112817 · Cited by 24

Risks and benefits of dermatological machine learning health care applications—an overview and ethical analysis

Willem, T., Krammer, S., Böhm, A. ‐., French, L. E., Hartmann, D., Lasser, T., & Buyx, A. (2022) · Journal of the European Academy of Dermatology and Venereology · doi:10.1111/jdv.18192 · Cited by 20

Not all sparks ignite the same flame: Firm AI innovation and ESG performance

Zhou, Z., Zhou, X., Zhang, X., & He, Q. (2025) · Journal of Business Research · doi:10.1016/j.jbusres.2025.115738 · Cited by 18

Transformer-based models for combating rumours on microblogging platforms: a review

Anggrainingsih, R., Hassan, G. M., & Datta, A. (2024) · Artificial Intelligence Review · doi:10.1007/s10462-024-10837-9 · Cited by 26

Green AI techniques for reducing energy consumption in AI systems

Khan, S., Naz, N. S., Mazhar, T., Tariq, M. U., Shahzad, T., Guizani, S., & Hamam, H. (2025) · Array · doi:10.1016/j.array.2025.100652 · Cited by 29

Time for change: compliance with RCS green theatre checklist—facilitators and barriers on the journey to net zero

Westwood, E., Walshaw, J., Boag, K., Chua, W., Dimashki, S., Khalid, H., Lathan, R., Wellington, J., Lockwood, S., & Yiasemidou, M. (2023) · Frontiers in Surgery · doi:10.3389/fsurg.2023.1260301 · Cited by 17

Sneaky Spikes: Uncovering Stealthy Backdoor Attacks in Spiking Neural Networks with Neuromorphic Data

Abad, G., Ersoy, O., Picek, S., & Urbieta, A. (2024) · Network and Distributed System Security Symposium · doi:10.14722/ndss.2024.24334 · Cited by 23

Explainable Automated Machine Learning for Credit Decisions: Enhancing Human Artificial Intelligence Collaboration in Financial EngineeringPreprint

Schmitt, M. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4716847 · Cited by 34

Health technology assessment framework for artificial intelligence-based technologies

Bidino, R. D., Daugbjerg, S. B., Papavero, S. C., Haraldsen, I., Cicchetti, A., & Sacchini, D. (2024) · International Journal of Technology Assessment in Health Care · doi:10.1017/s0266462324000308 · Cited by 16

Data hazards in synthetic biology

Zelenka, N., Cara, N. H. D., Sharma, K., Sarvaharman, S., Ghataora, J. S., Parmeggiani, F., Nivala, J., Abdallah, Z. S., Marucci, L., & Gorochowski, T. E. (2024) · Synthetic Biology · doi:10.1093/synbio/ysae010 · Cited by 16

A Review of Innovative Medical Rehabilitation Systems with Scalable AI-Assisted Platforms for Sensor-Based Recovery Monitoring

Boltaboyeva, A., Baigarayeva, Z., Иманбек, Б., Ozhikenov, К., Getahun, A. J., Aidarova, T., & Karymsakova, N. (2025) · Applied Sciences · doi:10.3390/app15126840 · Cited by 22

To be Artificial Intelligence for sustainability or not to be sustainable Artificial Intelligence

Rizzo, S. A. (2025) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2025.116063 · Cited by 15

The paradox of artificial intelligence and environmental performance: The critical role of energy transition and institutional quality

Zhang, A., Khan, D., & Chen, R. (2025) · Journal of Environmental Management · doi:10.1016/j.jenvman.2025.127330 · Cited by 15

Tres escenarios para la IA en educación: del apoyo responsable a la cocreación

García‐Peñalvo, F. J. (2025) · Education in the Knowledge Society (EKS) · doi:10.14201/eks.32932 · Cited by 17

Proportionality Principle for the Ethics of Artificial Intelligence

Karliuk, M. (2022) · AI and Ethics · doi:10.1007/s43681-022-00220-1 · Cited by 15

From Consumers to Critical Users: Prompty, an AI Literacy Tool for High School Students

Dennison, D. V., Garcia, R. C. C., Sarin, P., Wolf, J., Bywater, C., Xie, B., & Lee, V. R. (2024) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v38i21.30378 · Cited by 18

From bias to black boxes: understanding and managing the risks of AI – an actuarial perspective

du Preez, V., Bennet, S., Byrne, M., Couloumy, A., Das, A., Dessain, J., Galbraith, R., King, P., Mutanga, V., Schiller, F., et al. (2024) · British Actuarial Journal · doi:10.1017/s1357321724000060 · Cited by 15

Life cycle assessment of electric kick scooters: consolidating environmental impact quantification and concluding climate-friendly use options

Baumgartner, C., & Helmers, E. (2024) · Environmental Sciences Europe · doi:10.1186/s12302-024-00920-x · Cited by 15

Artificial intelligence in medicine and the pursuit of environmentally responsible science

Gaetani, M., Mazwi, M., Balaci, H., Greer, R., & Maratta, C. (2024) · The Lancet Digital Health · doi:10.1016/s2589-7500(24)00090-6 · Cited by 15

Situating the social issues of image generation models in the model life cycle: a sociotechnical approach

Katirai, A., García, N., Ide, K., Nakashima, Y., & Kishimoto, A. (2024) · AI and Ethics · doi:10.1007/s43681-024-00517-3 · Cited by 19

The impact of climate change on vulnerable populations in pediatrics: opportunities for AI, digital health, and beyond—a scoping review and selected case studies

Campbell, E. A., Holl, F., Marwah, H. K., Fraser, H., & Craig, S. (2025) · Pediatric Research · doi:10.1038/s41390-024-03719-x · Cited by 16

Identity of AI

Devedžić, V. (2022) · Discover Artificial Intelligence · doi:10.1007/s44163-022-00038-0 · Cited by 25

AI in Companies' Production Processes

Maldonado-Canca, L.-A., Cabrera-Sánchez, J.-P., Molina, A. M. C., & González, G. J. B. (2025) · Journal of Global Information Management · doi:10.4018/jgim.366653 · Cited by 19

A Study on Energy Consumption in AI-Driven Medical Image Segmentation

Prajwal, R., Pawan, S. J., Nazarian, S., Heller, N. E., Weight, C., Duddalwar, V. A., & Kuo, C. ‐. J. (2025) · Journal of Imaging · doi:10.3390/jimaging11060174 · Cited by 14

Use of AI as a Research Recruitment Tool: Ethical Elements, Considerations, and Recommendations

Rentzepis, P. J., Snyder, D. L., Baker, T. J., & Bierer, B. E. (2026) · The American Journal of Bioethics · doi:10.1080/15265161.2026.2649234 · Cited by 14

Peace and Prosperity for the Digital Age? The Colonial Political Economy of European AI Governance

Carmel, E., & Paul, R. (2022) · IEEE Technology and Society Magazine · doi:10.1109/mts.2022.3173340 · Cited by 17

The Global Impact of Artificial Intelligence

Alabdulatif, A. (2024) · World sustainability series · doi:10.1007/978-3-031-56292-1_21 · Cited by 14

The impact of smart city construction on achieving peak carbon neutrality: Evidence from 31 provinces in China

Yang, X., Zhang, D., & Masron, T. A. (2024) · Land Use Policy · doi:10.1016/j.landusepol.2024.107372 · Cited by 15

The impact of artificial intelligence on carbon market in China: Evidence from quantile-on-quantile regression approach

Jiang, W., Hu, Y., & Zhao, X. (2025) · Technological Forecasting and Social Change · doi:10.1016/j.techfore.2025.123973 · Cited by 16

Data-Efficient Estimation of Remaining Useful Life for Machinery With a Limited Number of Run-to-Failure Training Sequences

Sternharz, G., Elhalwagy, A., & Kalganova, T. (2022) · IEEE Access · doi:10.1109/access.2022.3226780 · Cited by 14

AI risk assessment using ethical dimensions

Tartaro, A., Panai, E., & Cocchiaro, M. Z. (2024) · AI and Ethics · doi:10.1007/s43681-023-00401-6 · Cited by 16

Generative AI guidelines by/for engineering undergraduates

Camacho-Zuñiga, C., Rodea-Sánchez, M. A., López, O. O., & Zavala, G. (2024) · 2024 IEEE Global Engineering Education Conference (EDUCON) · doi:10.1109/educon60312.2024.10578870 · Cited by 13

Measuring the Effectiveness of Carbon-Aware AI Training Strategies in Cloud Instances: A Confirmation Study

Vergallo, R., & Mainetti, L. (2024) · Future Internet · doi:10.3390/fi16090334 · Cited by 17

Landing AI on Networks: An Equipment Vendor Viewpoint on Autonomous Driving Networks

Rossi, D., & Zhang, L. (2022) · IEEE Transactions on Network and Service Management · doi:10.1109/tnsm.2022.3169988 · Cited by 16

The green paradox: The climate, environmental, and sustainability implications of artificial intelligence

Chouksey, A., Rajan, A. K., Gurjar, V., Tiwari, R., & Mishra, P. K. (2026) · Global Environmental Change Advances · doi:10.1016/j.gecadv.2025.100029 · Cited by 16

Action research at the BBC: Interrogating artificial intelligence with journalists to generate actionable insights for the newsroom

Jones, B., & Jones, R. (2025) · Journalism · doi:10.1177/14648849251317150 · Cited by 18

İklim değişikliği ve yapay zekâ: fırsatlar ve sorunlar

Tuğaç, Ç. (2023) · Hitit Sosyal Bilimler Dergisi · doi:10.17218/hititsbd.1240744 · Cited by 13

A Study of the Application of AI & ML to Climate Variation, with Particular Attention to Legal & Ethical Concerns

Joshi, M. N., Dixit, A. K., Saxena, S., Memoria, M., Choudhury, T., & Sar, A. (2024) · EAI Endorsed Transactions on Internet of Things · doi:10.4108/eetiot.5468 · Cited by 13

From crisis to prosperity: Leveraging robots, artificial intelligence, and service automation for sustainable tourism in Zimbabwe

Chiwaridzo, O. T., & Chiwaridzo, S. (2024) · Business Strategy & Development · doi:10.1002/bsd2.380 · Cited by 15

Smaller, faster, lower-power analog RRAM matrix computing circuits without performance compromise

Luo, Y., Zuo, P., Wang, S., Sun, Z., & Huang, R. (2025) · Science China Information Sciences · doi:10.1007/s11432-023-3990-4 · Cited by 12

Quantum Technologies in the Context of Climate Change: Emphasizing Sustainability in a Responsible Innovation Approach to Quantum Innovation

Root, D. (2025) · NanoEthics · doi:10.1007/s11569-025-00468-x · Cited by 13

Beyond AI as an environmental pharmakon: Principles for reopening the problem-space of machine learning's carbon footprint

Cellard, L., Parker, C., & Haines, F. (2025) · Environment and Planning E Nature and Space · doi:10.1177/25148486251332087 · Cited by 35

Accounting for the future: practice, Artificial Intelligence and regulation

Oprea, O., Hoinaru, R., Păcuraru-Ionescu, C.-P., & Neamţu, D.-M. (2022) · Proceedings of the ... International Conference on Business Excellence · doi:10.2478/picbe-2022-0076 · Cited by 12

Evaluation of Climate-Aware Metrics Tools for Radiology Informatics and Artificial Intelligence: Toward a Potential Radiology Ecolabel

Doo, F. X., Parekh, V. S., Kanhere, A., Savani, D., Tejani, A. S., Sapkota, A., & Yi, P. H. (2023) · Journal of the American College of Radiology · doi:10.1016/j.jacr.2023.11.019 · Cited by 12

The Role of Ethical AI in Fostering Harmonic Innovations that Support a Human-Centric Digital Transformation of Economy and Society

Ruffolo, M. (2022) · Lecture notes in networks and systems · doi:10.1007/978-3-030-81190-7_15 · Cited by 18

Does Machine Understanding Require Consciousness?

Pepperell, R. (2022) · Frontiers in Systems Neuroscience · doi:10.3389/fnsys.2022.788486 · Cited by 11

Trust and transparency in AI: industry voices on data, ethics, and compliance

McCormack, L., Bendechache, M., Lewis, D., & Huyskes, D. (2025) · AI & Society · doi:10.1007/s00146-025-02654-7 · Cited by 23

Effective Generative AI Implementation in Developing Country Universities

Camacho-Zúñiga, C. (2024) · IEEE Conference on Artificial Intelligence (CAI) · doi:10.1109/cai59869.2024.00093 · Cited by 12

UFLUX-GPP: A Cost-Effective Framework for Quantifying Daily Terrestrial Ecosystem Carbon Uptake Using Satellite Data

Zhu, S., Xu, J., Zeng, J., He, P., Wang, Y., Bao, S., Ma, J., & Shi, J. (2024) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2024.3439333 · Cited by 13

CompNET: Boosting image recognition and writer identification via complementary neural network post-processing

Zhao, B., Cao, X., Zhang, W., Liu, X., Miao, Q., & Li, Y. (2024) · Pattern Recognition · doi:10.1016/j.patcog.2024.110880 · Cited by 11

Analyzing the Nexus between AI Innovation and Ecological Footprint in Nordic Region: Impact of Banking Development and Stock Market Capitalization using Panel ARDL method

Shiam, S. A. A., Ridwan, M., Hasan, M. M., Akhter, A., Arefeen, S. M. S., Hossain, M. S., Abir, S. I., & Shoha, S. (2024) · Journal of Environmental Science and Economics · doi:10.56556/jescae.v3i3.973 · Cited by 24

Materiality and risk in the age of pervasive AI sensors

Sloane, M., Moss, E., Kennedy, S., Stewart, M. P., Warden, P., Plancher, B., & Reddi, V. J. (2025) · Nature Machine Intelligence · doi:10.1038/s42256-025-01017-7 · Cited by 16

Novel artificial intelligence-driven software significantly shortens the time required for annotation in computer vision projects

Hansen, U. S., Landau, E., Patel, M., & Hayee, B. (2021) · Endoscopy International Open · doi:10.1055/a-1341-0689 · Cited by 11

Carbon resilience calibration as a carbon management technology

Talebian, S. H., Jahanbakhsh, A., & Maroto‐Valer, M. M. (2023) · Frontiers in Energy Research · doi:10.3389/fenrg.2023.1089778 · Cited by 11

The carbon footprint of computational research

(2023) · Nature Computational Science · doi:10.1038/s43588-023-00506-2 · Cited by 11

Poster

Abad, G., Ersoy, O., Picek, S., Ramírez-Durán, V. J., & Urbieta, A. (2022) · ACM SIGSAC Conference on Computer and Communications Security (CCS) · doi:10.1145/3548606.3563532 · Cited by 10

Towards Artificial Intelligence in Sustainable Environmental Development

Taherdoost, H. (2023) · Artificial Intelligence Evolution · doi:10.37256/aie.4120232503 · Cited by 10

Artificial Intelligence and Human Rights

(2023) · Oxford University Press eBooks · doi:10.1093/law/9780192882486.001.0001 · Cited by 10

Soft cosine and extended cosine adaptation for pre-trained language model semantic vector analysis

Ijebu, F. F., Liu, Y., Sun, C., & Usip, P. U. (2024) · Applied Soft Computing · doi:10.1016/j.asoc.2024.112551 · Cited by 14

Wavelet-Infused Convolution-Transformer for Efficient Segmentation in Medical Images

Dutta, P., Mitra, S., & Roy, S. K. (2025) · IEEE Transactions on Systems Man and Cybernetics Systems · doi:10.1109/tsmc.2025.3539573 · Cited by 10

Does artificial intelligence improve energy productivity in China's industrial sector? Empirical evidence based on the spatial moderation model

Rong, J., Wang, W., & Zhang, H. (2023) · Energy & Environment · doi:10.1177/0958305x231177732 · Cited by 9

Significance of Emerging Technological Advancements in Transition of Green Economy

Rautela, R., Kumar, S., Pandey, S., Prakash, N., Malik, P. K., & Kumar, A. (2023) · Devices for Integrated Circuit (DevIC) · doi:10.1109/devic57758.2023.10134956 · Cited by 9

Artificial intelligence, sustainability and environmental impact. A narrative and bibliometric study

Camastra, F. D., & Vallejo, R. G. (2025) · Región Científica · doi:10.58763/rc2025355 · Cited by 20

Training Green AI Models Using Elite Samples

Alswaitti, M., Verdecchia, R., Danoy, G., Bouvry, P., & Pecero, J. E. (2025) · IEEE Transactions on Sustainable Computing · doi:10.1109/tsusc.2025.3544430 · Cited by 9

The development of carbon-neutral data centres in space

Aili, A., Choi, J. P., Ong, Y.-S., & Wen, Y. (2025) · Nature Electronics · doi:10.1038/s41928-025-01476-1 · Cited by 11

Time-series forecasting through recurrent topology

Chomiak, T., & Hu, B. (2024) · Communications Engineering · doi:10.1038/s44172-023-00142-8 · Cited by 9

Wild Swarms: Autonomous Drones for Environmental Monitoring and Protection

Saffre, F., Karvonen, H., & Hildmann, H. (2023) · Springer eBooks · doi:10.1007/978-3-031-44607-8_1 · Cited by 9

Comparison of Convolutional Neural Networks and Transformers for the Classification of Images of COVID-19, Pneumonia and Healthy Individuals as Observed with Computed Tomography

Ascencio-Cabral, A., & Reyes‐Aldasoro, C. C. (2022) · Journal of Imaging · doi:10.3390/jimaging8090237 · Cited by 11

YOLOv10 to Its Genesis: A Decadal and Comprehensive Review of The You Only Look Once SeriesPreprint

Sapkota, R., Qureshi, R., Flores-Calero, M. J., Badgujar, C., Nepal, U., Poulose, A., Zeno, P. J., Vaddevolu, U. B. P., Yan, P. H., & Karkee, M. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4874098 · Cited by 9

LLMs beyond the lab: the ethics and epistemics of real-world AI research

Mollen, J. (2024) · Ethics and Information Technology · doi:10.1007/s10676-024-09819-w · Cited by 11

Eco-pharma dilemma: Navigating environmental sustainability trade-offs within the lifecycle of pharmaceuticals – A comment

Moermond, C., Puhlmann, N., Pieters, L., Matharu, A. S., Boone, L., Dobbelaere, M. R., Proquin, H., Kümmerer, K., Ragas, A. M. J., Vidaurre, R., et al. (2024) · Sustainable Chemistry and Pharmacy · doi:10.1016/j.scp.2024.101893 · Cited by 10

The impact of artificial intelligence on carbon emissions inequality: evidence from China cities

Ma, D., Zhu, Y., & Lee, C. (2025) · Applied Economics · doi:10.1080/00036846.2025.2449621 · Cited by 8

Tissue concepts: Supervised foundation models in computational pathology

Nicke, T., Schäfer, J. R., Höfener, H., Feuerhake, F., Merhof, D., Kiessling, F. M., & Lotz, J. (2025) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2024.109621 · Cited by 17

AI in Marketing Management: Executive Perspectives from Companies

Maldonado-Canca, L.-A., Cabrera-Sánchez, J.-P., Robles, E. M. G., & Molina, A. M. C. (2024) · Marketing and Management of Innovations · doi:10.21272/mmi.2024.4-04 · Cited by 12

Pioneering Eco-Efficiency in Cloud Computing: The Carbon-Conscious Federated Reinforcement Learning (CCFRL) Approach

Seo, E., & Elmroth, E. (2025) · IEEE Internet of Things Journal · doi:10.1109/jiot.2024.3504260 · Cited by 8

AI-Image Generation in Research Interviews: Opportunities and Challenges

Nardon, L., Bruning, C., Valgardsson, S., & Busato, M. (2025) · International Journal of Qualitative Methods · doi:10.1177/16094069251333335 · Cited by 7

Big team science reveals promises and limitations of machine learning efforts to model physiological markers of affective experience

Coles, N. A., Perz, B., Behnke, M., Eichstaedt, J. C., Kim, S.-H., Vu, T. N., Raman, C., Tejada, J., Huynh, V. T., Zhang, G., et al. (2025) · Royal Society Open Science · doi:10.1098/rsos.241778 · Cited by 8

The Need for More Justice-Oriented Courses in Undergraduate Computer Science Curricula

Moudgalya, S. K., & Zeller, A. (2024) · ACM Technical Symposium on Computer Science Education (SIGCSE) · doi:10.1145/3626252.3630949 · Cited by 8

AI Art for Self-Interest or Common Good? Uncovering Value Tensions in Artists’ Imaginaries of AI Technologies

Jääskeläinen, P., Holzapfel, A., & Eriksson, E. (2024) · Designing Interactive Systems Conference · doi:10.1145/3643834.3661619 · Cited by 8

A Bregman Learning Framework for Sparse Neural NetworksPreprint

Bungert, L., Roith, T., Tenbrinck, D., & Burger, M. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2105.04319 · Cited by 16

AI-enabled ChatGPT’s carbon footprint and its use in the healthcare sector: a coin has two sides

Chakraborty, C., Pal, S., Bhattacharya, M., & Islam, M. A. (2023) · International Journal of Surgery · doi:10.1097/js9.0000000000000905 · Cited by 7

The Cost Of Training A Machine: Lighting The Way For a Climate-Aware Policy Framework That Addresses Artificial Intelligence's Carbon Footprint Problem

(2024) · FLASH - Fordham Law Archive of Scholarship & History (Fordham University)

The sustainability paradox: A systematic review of carbon footprints and decarbonization strategies for artificial intelligence in climate science

Khan, N., Sikandar, H., Falahat, M., & Akhtar, N. (2026) · Sustainable Environment · doi:10.1080/27658511.2026.2693347

Carbon Footprint of Training Large AI Models: Measuring and Proposing Mitigation Strategies for the Environmental Cost of AI

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