The growing energy footprint of artificial intelligence

Why Are There Still So Many Jobs? The History and Future of Workplace Automation

Autor, D. H. (2015) · Journal of Economic Perspectives · doi:10.1257/jep.29.3.3 · Cited by 3,917

The Carbon Footprint of Machine Learning Training Will Plateau, Then Shrink

Patterson, D., Gonzalez, J., Holzle, U., Le, Q., Liang, C., Munguia, L.-M., Rothchild, D., So, D. R., Texier, M., & Dean, J. (2022) · Computer · doi:10.1109/mc.2022.3148714 · Cited by 400

Estimating the Carbon Footprint of BLOOM, a 176B Parameter Language Model

Luccioni, A. S., Viguier, S., & Ligozat, A.-L. (2022) · arXiv · doi:10.48550/arxiv.2211.02001 · Cited by 378

A systematic review of Green AI

Verdecchia, R., Sallou, J., & Cruz, L. (2023) · WIREs Data Mining and Knowledge Discovery · doi:10.1002/widm.1507 · Cited by 375

Cryptocurrencies on the road to sustainability: Ethereum paving the way for Bitcoin

de Vries, A. (2022) · Patterns · doi:10.1016/j.patter.2022.100633 · Cited by 110

“Limitless Computation on a Finite Planet?” The Actually Existing Ecology of Generative AI

Collinson, I. (2026) · Future of business and finance · doi:10.1007/978-3-032-07605-2_19 · Cited by 1

Automating decarbonization: industrial robots and the manufacturing in resource-based cities

Chen, Z., Wang, H., Cai, W., & Wei, W. (2026) · Structural Change and Economic Dynamics · doi:10.1016/j.strueco.2026.03.016 · Cited by 1

Struggling With the Black Box: Mapping Bias and Agency in Using Generative Artificial Intelligence in Qualitative Method Development

Vigren, M., & Paredes, L. L. (2026) · Qualitative Inquiry · doi:10.1177/10778004261434487 · Cited by 1

Investigating fine-tuning versus zero-shot learning for general large language models when predicting cancer survival from initial oncology consultation documents

Phaterpekar, T., Zeng, Z., Mali, Y., Leung, B., Ho, C., Ng, R. T., Bates, A. T., & Nunez, J.-J. (2026) · ESMO Real World Data and Digital Oncology · doi:10.1016/j.esmorw.2026.100703 · Cited by 1

Nuclear clouds: the infrastructural convergence of AI and energy systems

Catanzariti, B., Rella, L., & Giacometti, A. (2026) · Journal of Cultural Economy · doi:10.1080/17530350.2026.2654538 · Cited by 1

Energy system science must look beyond net zero

Weinand, J. M. (2026) · Nature Reviews Clean Technology · doi:10.1038/s44359-026-00170-5 · Cited by 1

Using degree apprenticeships to shape the future of traditional undergraduate degrees

Macaulay, E., Jackson, J., White, C., & Bevan, A. J. (2026) · Higher Education Skills and Work-based Learning · doi:10.1108/heswbl-07-2025-0290 · Cited by 1

Extra terra nullius: Off-worlding the externalities of AI, Bitcoin mining and cloud computing with Orbital Data Centres

Howson, P. (2026) · Energy Research & Social Science · doi:10.1016/j.erss.2026.104725 · Cited by 1

Net electricity savings from artificial intelligence depend on deployment efficiency in China’s power system

Zhou, K., Yang, Z., & Hu, R. (2026) · Communications Sustainability · doi:10.1038/s44458-026-00080-4 · Cited by 1

AI Data Center Load Forecasting: A Liquid Neural Network Approach

Mughees, M., Li, Y., Lin, Z., Chen, Y., & Li, Y. (2026) · IEEE Transactions on Smart Grid · doi:10.1109/tsg.2026.3692769 · Cited by 2

Artificial intelligence enters higher education: does research drive carbon reduction or accelerate emissions?

Zhang, W., Song, X., Lin, M., & Vasile, E. (2026) · International Journal of Sustainability in Higher Education · doi:10.1108/ijshe-11-2025-1391 · Cited by 1

Artificial intelligence’s hidden environmental and public-health risks: Should AI be considered a public health threat?

Domingo, J. L., & Shomar, B. (2026) · Human and Ecological Risk Assessment An International Journal · doi:10.1080/10807039.2026.2670401 · Cited by 1

Evaluating and predicting the carbon footprint of training and inference for large-scale AI models in China

Zhou, K., Yang, Z., & Gao, Y. (2026) · Technological Forecasting and Social Change · doi:10.1016/j.techfore.2026.124739 · Cited by 1

Upcycling battery scrap into commercial-grade graphite anode materials via coal tar-assisted granulation

Choi, J., Kim, T. H., Song, G., Park, S.-M., Jung, J. C., An, J., & Yang, I. (2026) · Chemical Engineering Journal · doi:10.1016/j.cej.2026.177961 · Cited by 1

Energy Consumption, Economic Growth, and Climate Change Dynamics in Southeast European Countries

Muço, K., Karma, E., & Nguyen, L. (2026) · Sustainability · doi:10.3390/su18115776 · Cited by 1

Artificial intelligence for climate change mitigation and adaptation: A domain-structured review of methods, applications, and research gaps for seven high-impact domains

Dang, L. M., Danish, S., Fayaz, M., Arzu, G. E., Nguyen, A., Song, H.-K., & Moon, H. (2026) · Process Safety and Environmental Protection · doi:10.1016/j.psep.2026.109102 · Cited by 2

Learning continually with representational drift

van der Veldt, S., van de Ven, G. M., Moorman, S., & Etter, G. (2026) · Trends Open · doi:10.1016/j.treopn.2026.04.013 · Cited by 3

Overall geometric optimization of hierarchic manifold microchannel heat sinks for > 3000 W chips with extremely low pumping power consumption

Wang, Z., & Li, J. (2026) · International Communications in Heat and Mass Transfer · doi:10.1016/j.icheatmasstransfer.2026.111839 · Cited by 1

Relational AI in Education: Reciprocity, Participatory Design, and Indigenous Worldviews

Martínez‐Maldonado, R., Echeverría, V., Hawes, J., Kim, Y. J., Maddigan, Z., Milesi, M. E., Nelson, T., & Tsai, Y. (2026) · Communications in computer and information science · doi:10.1007/978-3-032-29791-4_29 · Cited by 1

Sliding Ferroelectricity Induced Spatio-Segregation of Band-Edge Carriers in Bilayer HfN 2

Kishore, A., & Mahapatra, S. (2026) · The Journal of Physical Chemistry C · doi:10.1021/acs.jpcc.6c01931 · Cited by 1

Phosphoric acid-engineered biochar for efficient uranium separation: From actual uranium wastewater treatment to molecular-scale insights

Tan, Z., Liang, Y., Chen, W., Xu, Z., Gao, Y. N., Jiang, Y., Wu, H., Kang, M., Wang, J., Gök, C., et al. (2026) · Separation and Purification Technology · doi:10.1016/j.seppur.2026.139258 · Cited by 1

ChatGPT and Large Language Models for English Professors: Getting It Right

Sheu, C. J. (2026) · College English · doi:10.58680/ce2026883346 · Cited by 1

Life cycle assessment of a generative artificial intelligence system: the case of a radio application

Alexandroff, R., Furberg, A., Westberg, A., Amerotti, M., & Holzapfel, A. (2026) · The International Journal of Life Cycle Assessment · doi:10.1007/s11367-026-02713-1 · Cited by 1

AI Agents and the Progressive Intermediation Between Users and Online Services: A Critical Analysis

Rondina, M., Vetrò, A., Martin, J. C. D., Bargis, M., & Elia, G. (2026) · Future Internet · doi:10.3390/fi18080419 · Cited by 1

Large language medicine: defining a new paradigm in human health

Guni, A., Hu, W., Morley, J., Cohen, I. G., Barnaghi, P. M., & Ashrafian, H. (2026) · Frontiers in Science · doi:10.3389/fsci.2026.1810095 · Cited by 3

Towards Green AI by Reducing Training Effort of Recurrent Neural Networks Using Hyper-Parameter Optimization with Dynamic Stopping Criteria

Podgorelec, V., Fister, I., & Vrbančič, G. (2024) · IEEE International Symposium on Intelligent Systems and Informatics (SISY) · doi:10.1109/sisy62279.2024.10737555 · Cited by 1

Review Essays

Lingold, M. C., Wyatt, C., Onciul, B., Everson, A., Luria, J. N., Zabalueva, O., Webb, M. R., Kim, S., & Selwood, S. (2024) · Museum Worlds · doi:10.3167/armw.2024.120117 · Cited by 1

Enhancing Personal Identity Proofing Services Through AI for a Sustainable Digital Society in Korea

Kim, J. (2025) · Sustainability · doi:10.3390/su172310486 · Cited by 2

The Social Structure of Scientific Evaluation: AI, Benchmarking, and the Deep Learning MonoculturePreprint

Koch, B., & Peterson, D. (2024) · SocArXiv (OSF Preprints) · doi:10.31235/osf.io/s4qdy · Cited by 1

New Perspectives on Growth and Sustainability. Glimpses Into the AI Industrial RevolutionPreprint

Contucci, P., Osabutey, G., & Zimmaro, F. (2024) · Qeios · doi:10.32388/wo5yeq · Cited by 1

Energy Consumption Ramifications of U.S.-China AI CompetitionPreprint

Guo, J., Duan, Z., Flanigen, C., Hsu, E., Simon, V., & Floridi, L. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4839772 · Cited by 1

Watts and Bots: The Energy Implications of AI AdoptionPreprint

Harding, A. R., & Moreno‐Cruz, J. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4991908 · Cited by 1

From Pixels to Progress

Hsu, A. (2025) · Oxford University Press eBooks · doi:10.1093/9780198945215.003.0023 · Cited by 1

Adverse Environmental and Public Health Effects of Artificial Intelligence: A Narrative ReviewPreprint

Domingo, J. L. (2026) · Qeios · doi:10.32388/x3o2n6.3 · Cited by 1

Grid topology and the spatial cost of AI data-centre siting

Deng, X., Poletti, S., & Hazledine, T. (2026) · Research Square · doi:10.21203/rs.3.rs-10300906/v1

Ethical Responsibilities in the Backends of Media and Digital Technologies

Feng, Y. (2023) · Journal of Media Ethics · doi:10.1080/23736992.2024.2296194

Making machine learning on HPC systems cost-effective and carbon-friendly

Li, B. (2024) · doi:10.17760/d20659754

An Empirical Approach to Understanding the Energy Footprint of Data Centers and AiPreprint

Tan, X., & Souza, A. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4870717

E

Bendel, O. (2024) · Springer eBooks · doi:10.1007/978-3-658-44963-6_5

Application of the Optimised Pulse Width Modulation (PWM) Based Encoding-Decoding Algorithm for Forecasting with Spiking Neural Networks (SNNs)

Lucas, S., & Portillo, E. (2024) · doi:10.3390/engproc2024068041

WIP: Developing a Framework for Ethical Integration of Technology in Instruction

Choi, H. (2024) · ASEE Annual Conference & Exposition · doi:10.18260/1-2--48294

IA Generativa e remix: Diferença e repetição

Gunkel, D. J. (2024) · Esferas · doi:10.31501/esf.v2i30.15219

Brains Over Brawn: Small AI Labs in the Age of Datacenter-Scale Compute

Put, J., Michiels, N., Vanherle, B., & Zoomers, B. (2024) · Communications in computer and information science · doi:10.1007/978-3-031-66705-3_2

Unveiling the Decision-Making Process in Reinforcement Learning with Genetic Programming

Eberhardinger, M., Rupp, F., Maucher, J., & Maghsudi, S. (2024) · Lecture notes in computer science · doi:10.1007/978-981-97-7181-3_28

Optimización y extensión del algoritmo de codificación-decodificación basado en PWM para Redes Neuronales de Impulsos

Lucas, S., Portillo, E., & Cabanes, I. (2024) · Revista Iberoamericana de Automática e Informática Industrial RIAI · doi:10.4995/riai.2024.20836

Automatic Text Summarization Based on Transportation Network and Word Mover’s Distances Embeddings: A Toy Experiment

Roldán-Ahumada, J.-A., Avendaño‐Garrido, M. L., & Torres‐Moreno, J. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-75543-9_14

Organoid Computing: Leveraging Organoid Neural Networks for Artificial Intelligence

Li, K., Gu, L., Cai, H., & Guo, F. (2024) · Collaborative bioethics · doi:10.1007/978-3-031-72371-1_12 · Cited by 1

Inteligencia Artificial: ni muy automatizada, ni muy ecológica

California, U. A. D. B., Chavez, A. D. J. G., García‐Macías, J. A., & de Ensenada, C. D. I. C. Y. D. E. S. (2024) · doi:10.61728/ae24001021

Text Classification based on Spiking Neural Network with LSTM Conversion

Zhou, S. (2024) · Applied and Computational Engineering · doi:10.54254/2755-2721/99/20251772

Navigating European Healthcare Regulations Through a Historical Lens

Pecchia, L., & Maccaro, A. (2024) · Human perspectives in health sciences and technology · doi:10.1007/978-3-031-76961-0_7

Future of Neural Networks and Energy Consumption Aspects

Talanov, M. O., Bojić, L., Žunić, D., Samardžić, N., & Medojević, M. (2024) · Studies in computational intelligence · doi:10.1007/978-3-031-76516-2_40

KI: Die Innovation, die gar keine ist?

Geuter, J. (2024) · PROKLA Zeitschrift für kritische Sozialwissenschaft · doi:10.32387/prokla.v54i217.2165 · Cited by 1

Parallelization strategies for high-performance and energy-efficient epidemic spread simulations

Cagigas-Muñiz, D., Díaz-del-Río, F., Sevillano, J. L., & Guisado-Lizar, J.-L. (2025) · Simulation Modelling Practice and Theory · doi:10.1016/j.simpat.2024.103059

Quantum ion coherence in biological ion channels

Chen, L., & Xia, F. (2025) · Science China Materials · doi:10.1007/s40843-024-3231-x

Phenomenon of white superevaporation of water and its application for enhancing energy efficiency in dark/solar water purification, power generation, and generative AI

Fang, R., Li, Y., Zheng, J., Yan, Z., Pan, N., Chen, X., Zhao, X. M., Chen, Q., Deng, Y., Sun, Z., et al. (2025) · Desalination · doi:10.1016/j.desal.2025.118695

Guest editorial: Ethics for artificial intelligence: A game based- framework for physiotherapists

Laurijssen, S. J. M., Dijkstra, M. W. M. C. S., van den Berg, G., Hanskamp, M., & Rijken, N. H. M. (2025) · Journal of Back and Musculoskeletal Rehabilitation · doi:10.1177/10538127251316945

Automation and Explainability: Supervised Machine Learning with Text Data

Wulff, P., Kubsch, M., & Krist, C. (2025) · Springer texts in education · doi:10.1007/978-3-031-74227-9_12

ARTIFICIAL INTELLIGENCE: EFFECTS ON SOCIETY, THE ECONOMY, AND ITS REGULATION

Vaselić, D., Vojvodić, N., & Jovović, J. (2024) · STED JOURNAL · doi:10.7251/sted2401034v

Moor on Ethics for Emerging Technologies: Some Environmental Considerations

Weckert, J. (2025) · Minds and Machines · doi:10.1007/s11023-025-09721-4

The Declaration of Helsinki Must Address the Challenges of Artificial Intelligence

Armitage, R. C. (2025) · Journal of Evaluation in Clinical Practice · doi:10.1111/jep.70065

Medienbildung für eine nachhaltige Entwicklung: Nachhaltigkeit als Gegenstand der Medienpädagogik und Kompetenzförderung in nachhaltigen Medienpraktiken

Kannengießer, S., & Bozdağ, Ç. (2025) · Medien, Kultur, Kommunikation · doi:10.1007/978-3-658-47029-6_20

Recognizing Selected Features in the News Text

Kaczmarek, A., Tobolewski, F., Ganzha, M., Paprzycki, M., & Wróblewska, A. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-86193-2_17

Privacy-Preserving Large Language Model for Matching Findings and Tracking Interval Changes in Longitudinal Radiology Reports

Mathai, T. S., Kim, B., Stroie, O. M., & Summers, R. M. (2025) · Journal of Imaging Informatics in Medicine · doi:10.1007/s10278-025-01478-7

Prolegomena to Any Future Device Physics [Point of View]

Friedman, A. L., & Hanbicki, A. T. (2024) · Proceedings of the IEEE · doi:10.1109/jproc.2025.3541563 · Cited by 1

Making Sense of Artificial Intelligence for Global Environmental Governance : Ideas, Power, and Policy Pathways

Francisco, M. (2025) · Linköping studies in arts and science · doi:10.3384/9789181181159

Kännykkä, pilvi ja tekoäly?

Paukkonen, N. (2025) · Muinaistutkija · doi:10.61258/mt.149485

Bionerf: Biologically Plausible Neural Radiance Fields for View SynthesisPreprint

Júnior, L. A. P., Rodrigues, D., Jodas, D. S., da Costa, K. A. P., Adeel, A., & Papa, J. P. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5243474

Medientheorie des Verkehrs

Schabacher, G. (2025) · Springer eBooks · doi:10.1007/978-3-658-38128-8_25-1

Providing Load Flexibility by Reshaping Power Profiles of Gpu Clusters Running Large Language Model WorkloadsPreprint

Yi, W., Guo, Q., & Chen, M. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5253797

Information Technology for Sustainability: The Hidden Cost of the Digital Era

Lemke, C. (2025) · Future of business and finance · doi:10.1007/978-3-031-89549-4_9

Building Green Generative AI: An Ecosystem-Wide Approach to Environmental Sustainability

Helms, F., Hönemann, K., & Wiesche, M. (2025) · Bled eConference · doi:10.18690/um.fov.4.2025.38

Oiso: Outlier-Isolated Data Format for Low-Bit Large Language Model Quantization

Zou, L., Yin, S., Li, M., Wang, M., Bai, C., Zhao, W., & Yu, B. (2025) · IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems · doi:10.1109/tcad.2025.3585023

Sensibiliser les étudiants à la sobriété numérique dans la gestion de projets en humanités numériques

Ripoll, É. (2025) · Humanités numériques · doi:10.4000/1498v

Frugal AI: The New Era of Sustainable Deep LearningPreprint

Bouali, Y. L., Ahmed, O. B., Bradai, A., & Mazouzi, S. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5334656

AI Breakthroughs in Carbon Emission Reduction

Thục, U. N. C., Virgilio, F. D., & Das, S. (2025) · Springer eBooks · doi:10.1007/978-981-96-8015-3_4

Spiking Alternatives for the Leaky Integrate-and-Fire Neuron: Applications in Cybersecurity and Financial Threats

Perdigão, D., Antunes, F., Silva, C., & Ribeiro, B. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-99565-1_31 · Cited by 2

Exploring subthreshold processing for next-generation TinyAI

Nakhle, F., Harfouche, A., Karam, H., & Tserolas, V. (2025) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2025.1638782

A Privacy-Preserving Temporal-Spatial Framework of Optimal Llm Workload Management for Data Center Demand ResponsePreprint

You, H., Jin, Z., Zhou, Y., Flynn, D., Lou, C., & Yang, J. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5357701

Artificial Intelligence: Benefits and Flaws

Yarali, A., Kelley, J., & Taylor, L. (2025) · IEEE World AI IoT Congress (AIIoT) · doi:10.1109/aiiot65859.2025.11105338

Notes

Maçães, B. (2025) · doi:10.1017/9781009397414.008

Analyzing the Resource Utilization of Lambda Functions on Mobile Devices: Case Studies on Kotlin and Swift

Ejimuda, C. U., Longhitano, G., & Rawassizadeh, R. (2025) · IEEE Pervasive Computing · doi:10.1109/mprv.2025.3572675

New Perspectives on Growth and Sustainability. Glimpses into the AI Industrial RevolutionPreprint

Contucci, P., Osabutey, G., & Zimmaro, F. (2025) · Qeios · doi:10.32388/wo5yeq.2

Produktlebenszyklus und Kreislaufwirtschaft

Lachmayer, R., Wurst, J., & Thelemann, J. (2025) · Springer eBooks · doi:10.1007/978-3-662-65265-7_4

Redefining the Translation Profession: The Impact of Artificial Intelligence

Bakarić, M. B. (2025) · International Convention on Information and Communication Technology, Electronics and Microelectronics · doi:10.1109/mipro65660.2025.11131716

Multivariate anomaly detection and root cause analysis of energy issues in microservice-based systems

Sanchez, B. R., Giamattei, L., Guerriero, A., Pietrantuono, R., & Malavolta, I. (2025) · Journal of Systems and Software · doi:10.1016/j.jss.2025.112626 · Cited by 1

Sustainable Smart Warehousing: Challenges, Opportunities, and Future Directions in the Era of Industry 5.0Preprint

Raghupatruni, J. K., Inkulu, A. K., Pujari, S., Eswaran, M., Chiranjibi, C., & Bahubalendruni, M. V. A. R. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5519197

Future Impacts of Industry 4.0 on Labour Markets and Production Reshoring, Concentration and Sustainability: A Prospective Literature Review

Buzzelli, G., & Nicoli, F. (2025) · Global Society · doi:10.1080/13600826.2025.2555174

Talk is Cheap, Energy is Not: Towards a Green, Context-Aware Metrics Framework for Automatic Speech Recognition

Ulan, M., Husom, E. J., & den Abeele, J. V. (2025) · Lecture notes in computer science · doi:10.1007/978-3-032-06118-8_3

A Deep Dive into FAIRness Assessment: UReFM, a Formal Framework for Representing, Analyzing and Comparing Measures

Lamarre, P., Andersen, J., Alban Gaignard, A., & Cazalens, S. (2025) · Lecture notes in computer science · doi:10.1007/978-3-662-72116-2_4

Energy-Optimal Control of Generalized Laguerre Neural Network

Hou, C. (2025) · IEEE Transactions on Automation Science and Engineering · doi:10.1109/tase.2025.3617782

Convivial AI? Developing a societal impact analysis grid for assessing artificial intelligence in Earth observation

Rehak, R., Ullrich, A., Hamm, A., Zehner, N., Mühlhoff, R., & Pütz, J. (2025) · Sustainability Science Practice and Policy · doi:10.1080/15487733.2025.2568274

Efficient and Secure GANs: A Survey on Privacy-Preserving and Resource-Aware Models

Apostolou, N. E., Balourdou, E. V., Mouratidou, M., Tsalera, E., Voyiatzis, I., Papadakis, A. E., & Σαμαράκου, Μ. (2025) · Applied Sciences · doi:10.3390/app152011207 · Cited by 2

Oversight of AI’S Dual Impact on Sustainability

Torre, F., Engstam, L. H., & Teigland, R. (2025) · Business guides on the go · doi:10.1007/978-3-032-04087-9_5

Quantum Neural Networks

Du, Y., Wang, X., Guo, N., Yu, Z., Qian, Y., Zhang, K., Hsieh, M.-H., Rebentrost, P., & Tao, D. (2025) · Springer eBooks · doi:10.1007/978-981-95-1284-3_4

Breaking Bond‐Strain Lockstep in Multielectron Anodes

Sun, S., Fang, C., Pei, F., Xu, S., Yuan, J., Wang, W., & Li, R. (2025) · Advanced Materials · doi:10.1002/adma.202517295

Implementation of transformer-based LLMs with large-scale optoelectronic neurons on a CMOS compatible platform

Na, N., Cheng, C.-H., Hsu, S.-C., Lin, C.-C., Lin, C.-C., Na, N. Y., Shieh, A. I., Chen, E., Rong, H., & Soref, R. A. (2026) · APL Machine Learning · doi:10.1063/5.0313015 · Cited by 1

Watts and bots: the energy implications of AI adoption

Harding, A. R., & Moreno‐Cruz, J. (2025) · Environmental Research Letters · doi:10.1088/1748-9326/ae0e3b

IT, Digitalisierung und Gesellschaft

Franzetti, C. (2025) · Springer eBooks · doi:10.1007/978-3-662-71428-7_17

Pool boiling enhancement using engineered nucleation sites

Viswanath, P., Kułakowski, T. K., Zhou, Y., & Adera, S. (2025) · I-THERM :/I-THERM - Intersociety Conference on Thermal Phenomena in Electronic Systems · doi:10.1109/itherm55376.2025.11235766

Large-scale, High-performance Protonic Ceramic Electrochemical Cells Fabricated by Inkjet PrintingPreprint

Kang, S. M., Kim, Y. S., Min, K. C., Kim, S. B., Shin, D., Lee, W., Hong, J., Han, G. D., & Shim, J. H. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5839960

Artificial intelligence: Supply chain constraints and energy implications

de Vries, A. (2025) · Joule · doi:10.1016/j.joule.2025.101961 · Cited by 21

Digital Transition and Artificial Intelligence for Good: Towards Ethical and Sustainable Energy Consumption?

Hill, D. R. C. (2025) · International Electronics Symposium (IES) · doi:10.1109/ies67184.2025.11161277

The Hidden Environmental Cost of Artificial Intelligence: Energy and Water Consumption

Ominga, Y. (2026) · Figshare · doi:10.6084/m9.figshare.31536277

AI proliferation will have serious environmental costs

Analytica, O. (2023) · Emerald expert briefings · doi:10.1108/oxan-ga281880 · Cited by 1

Can Exponential Technologies Push Global Warming Above 2C? AI, Bitcoin, and Energy ConsumptionPreprint

Ibañez, J. I. (2023) · SSRN Electronic Journal · doi:10.2139/ssrn.4504604 · Cited by 1