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

Integrating industry 4.0 for enhanced sustainability: Pathways and prospects

Khan, M. I., Yasmeen, T., Khan, M., Hadi, N. U., Asif, M., Farooq, M., & Al‐Ghamdi, S. G. (2024) · Sustainable Production and Consumption · doi:10.1016/j.spc.2024.12.012 · Cited by 255

ChatGPT Needs SPADE (Sustainability, PrivAcy, Digital divide, and Ethics) Evaluation: A Review

Khowaja, S. A., Khuwaja, P., Dev, K., Wang, W., Nkenyereye, L., & Fortino, G. (2024) · Cognitive Computation · doi:10.1007/s12559-024-10285-1 · Cited by 235

Making AI Less 'Thirsty'

Li, P., Yang, J., Islam, M. A., & Ren, S. (2025) · Communications of the ACM · doi:10.1145/3724499 · Cited by 176

Large language models for reticular chemistry

Zheng, Z., Rampal, N., Inizan, T. J., Borgs, C., Chayes, J. T., & Yaghi, O. M. (2025) · Nature Reviews Materials · doi:10.1038/s41578-025-00772-8 · Cited by 134

EU AI sovereignty: for whom, to what end, and to whose benefit?

Mügge, D. (2024) · Journal of European Public Policy · doi:10.1080/13501763.2024.2318475 · Cited by 160

A comprehensive review of machine learning-based state of health estimation for lithium-ion batteries: data, features, algorithms, and future challenges

Wang, Y., Guo, S., Cui, Y., Deng, L., Zhao, L., Li, J., & Wang, Z. (2025) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2025.116125 · Cited by 107

Environmental impact and net-zero pathways for sustainable artificial intelligence servers in the USA

Xiao, T., Nerini, F. F., Matthews, H. D., Tavoni, M., & You, F. (2025) · Nature Sustainability · doi:10.1038/s41893-025-01681-y · Cited by 121

Estimating the environmental impact of Generative-AI services using an LCA-based methodology

Berthelot, A., Caron, E., Jay, M., & Lefèvre, L. (2024) · Procedia CIRP · doi:10.1016/j.procir.2024.01.098 · Cited by 103

The mechanisms of AI hype and its planetary and social costs

Markelius, A., Wright, C., Kuiper, J., Delille, N., & Kuo, Y.-T. (2024) · AI and Ethics · doi:10.1007/s43681-024-00461-2 · Cited by 117

How much energy will AI really consume? The good, the bad and the unknown

Chen, S. (2025) · Nature · doi:10.1038/d41586-025-00616-z · Cited by 78

Metal–insulator–semiconductor photoelectrodes for enhanced photoelectrochemical water splitting

Wei, S., Xia, X., Bi, S., Hu, S., Wu, X., Hsu, H., Zou, X., Huang, K., Zhang, D. W., Sun, Q., et al. (2024) · Chemical Society Reviews · doi:10.1039/d3cs00820g · Cited by 78

Applied Artificial Intelligence in Materials Science and Material Design

Chávez‐Ángel, E., Eriksen, M., Castro‐Álvarez, A., García, J. H., Botifoll, M., Ávalos‐Ovando, Ó., Arbiol, J., & Mugarza, A. (2025) · Advanced Intelligent Systems · doi:10.1002/aisy.202400986 · Cited by 84

Stellaris: A high-field quasi-isodynamic stellarator for a prototypical fusion power plant

Lion, J., Anglès, J.-C., Bonauer, L., Navarro, A. B., Cadena, S. A., Davies, R., Drevlak, M., Foppiani, N., Geiger, J. V., Goodman, A. G., et al. (2025) · Fusion Engineering and Design · doi:10.1016/j.fusengdes.2025.114868 · Cited by 74

Dendrites endow artificial neural networks with accurate, robust and parameter-efficient learning

Chavlis, S., & Poirazi, P. (2025) · Nature Communications · doi:10.1038/s41467-025-56297-9 · Cited by 77

The role of nuclear energy and baseload demand in capacity expansion planning for low-carbon power systems

Hjelmeland, M., Nøland, J. K., Backe, S., & Korpås, M. (2024) · Applied Energy · doi:10.1016/j.apenergy.2024.124366 · Cited by 61

Investigating Gender and Racial Biases in DALL-E Mini Images

Cheong, M., Abedin, E., Ferreira, M., Reimann, R., Chalson, S., Robinson, P., Byrne, J., Ruppanner, L., Alfano, M., & Klein, C. (2024) · ACM Journal on Responsible Computing · doi:10.1145/3649883 · Cited by 57

Revisit the environmental impact of artificial intelligence: the overlooked carbon emission source?

Yang, Y., Wang, J., Liu, Y., Yu, P., Wang, D., Zheng, P., & Zhang, M. (2024) · Frontiers of Environmental Science & Engineering · doi:10.1007/s11783-024-1918-y · Cited by 83

Search still matters: information retrieval in the era of generative AI

Hersh, W. (2024) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocae014 · Cited by 65

Artificial Intelligence for Alleviating Energy Poverty: Pathways Toward Sustainable and Renewable Energy Transitions

Wang, Q., Sun, T., & Li, R. (2025) · Sustainable Development · doi:10.1002/sd.70388 · Cited by 56

Will AI accelerate or delay the race to net-zero emissions?

Luers, A., Koomey, J., Masanet, E., Gaffney, O., Creutzig, F., Lavista Ferres, J., & Horvitz, E. (2024) · Nature · doi:10.1038/d41586-024-01137-x · Cited by 45

A collective AI via lifelong learning and sharing at the edge

Soltoggio, A., Ben-Iwhiwhu, E., Braverman, V., Eaton, E., Epstein, B., Ge, Y., Halperin, L., How, J., Itti, L., Jacobs, M. A., et al. (2024) · Nature Machine Intelligence · doi:10.1038/s42256-024-00800-2 · Cited by 51

Precision Medicine—Are We There Yet? A Narrative Review of Precision Medicine’s Applicability in Primary Care

Evans, W. R. H., Meslin, E. M., Kai, J., & Qureshi, N. (2024) · Journal of Personalized Medicine · doi:10.3390/jpm14040418 · Cited by 50

‘AI coaching’: democratising coaching service or offering an ersatz?

Bachkirova, T., & Kemp, R. (2024) · Coaching: An International Journal of Theory, Research and Practice · doi:10.1080/17521882.2024.2368598 · Cited by 41

Can we disrupt the momentum of the AI colonization of science education?

Avraamidou, L. (2024) · Journal of Research in Science Teaching · doi:10.1002/tea.21961 · Cited by 56

Resource-efficient Algorithms and Systems of Foundation Models: A Survey

Xu, M., Cai, D., Yin, W., Wang, S., Jin, X., & Liu, X. (2024) · ACM Computing Surveys · doi:10.1145/3706418 · Cited by 255

Efficient and Green Large Language Models for Software Engineering: Literature Review, Vision, and the Road Ahead

Shi, J., Yang, Z., & Lo, D. (2025) · ACM Transactions on Software Engineering and Methodology · doi:10.1145/3708525 · Cited by 43

Open and remotely accessible Neuroplatform for research in wetware computing

Jordan, F. D., Kutter, M., Comby, J.-M., Brozzi, F., & Kurtys, E. (2024) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2024.1376042 · Cited by 45

Will Energy-Hungry AI Create a Baseload Power Demand Boom?

Nøland, J. K., Hjelmeland, M., & Korpås, M. (2024) · IEEE Access · doi:10.1109/access.2024.3440217 · Cited by 64

Efficient two-phase liquid cooling and optimization technology on the chip side based on high-power data centers: Summary and prospects

Chen, X., Hu, C., Liang, J. Y., Li, J., Lu, L., Wu, Y. C., & Zang, J. (2025) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2025.115994 · Cited by 42

To better understand AI’s growing energy use, analysts need a data revolution

Masanet, E. R., Lei, N., & Koomey, J. (2024) · Joule · doi:10.1016/j.joule.2024.07.018 · Cited by 44

SnS‐Facilitated ZnAlSnO‐Based Fully Optically Modulated Artificial Synaptic Device for Image Processing

Yang, R., Hu, D., Chen, Q., Wang, Z., Lu, B., Ye, Z., Li, X., & Lü, J. (2024) · Advanced Functional Materials · doi:10.1002/adfm.202414210 · Cited by 38

Towards an Energy Consumption Index for Deep Learning Models: A Comparative Analysis of Architectures, GPUs, and Measurement Tools

Aquino-Brítez, S., García‐Sánchez, P., Ortíz, A., & Aquino-Brítez, D. (2025) · Sensors · doi:10.3390/s25030846 · Cited by 52

Superdurable, Flexible Ceramic Nanofibers for Sustainable Passive Radiative Cooling

Chen, D.-C., Hwang, C.-W., Chang, C. Y., Kuo, C.-L., Chen, H., Lan, P., Tsai, M., Wang, T., & Wan, D. (2025) · ACS Nano · doi:10.1021/acsnano.5c05958 · Cited by 38

Variational quantum circuit learning-enabled robust optimization for AI data center energy control and decarbonization

Ajagekar, A., & You, F. (2024) · Advances in Applied Energy · doi:10.1016/j.adapen.2024.100179 · Cited by 41

AI‐Assisted Diagnosis and Treatment Planning — A Discussion of How AI Can Assist Healthcare Professionals in Making More Accurate Diagnoses and Treatment Plans for Diseases

Rashid, M. U., & Sharma, M. (2025) · doi:10.1002/9781394278695.ch14 · Cited by 37

Understanding the Environmental Impact of Generative AI Services

Berthelot, A., Caron, E., Jay, M., & Lefèvre, L. (2025) · Communications of the ACM · doi:10.1145/3725984 · Cited by 44

Green and intelligent: the role of AI in the climate transition

Stern, N. H., Romani, M., Pierfederici, R., Braun, M., Barraclough, D., Lingeswaran, S., Weirich-Benet, E., & Niemann, N. (2025) · npj Climate Action · doi:10.1038/s44168-025-00252-3 · Cited by 36

Techno-economic performance of reservoir thermal energy storage for data center cooling system

Oh, H., Jin, W., Peng, P., Winick, J. A., Sickinger, D., Sartor, D., Zhang, Y., Beckers, K. F., Kitz, K., Acero-Allard, D., et al. (2025) · Applied Energy · doi:10.1016/j.apenergy.2025.125858 · Cited by 40

Sustainability-Oriented Innovation in the Textile Manufacturing Industry: Pre-Consumer Waste Recovery and Circular Patterns

Butturi, M. A., Neri, A., Mercalli, F., & Gamberini, R. (2025) · Environments · doi:10.3390/environments12030082 · Cited by 34

Sustainable AI infrastructure: A scenario-based forecast of water footprint under uncertainty

Herrera, M., Xie, X., Menapace, A., Zanfei, A., & Brentan, B. (2025) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2025.146528 · Cited by 50

AI-orientation and company climate action: The moderating role of dependency structure and innovation capability

Zeng, F., Yihong, G., Fan, Q.-J., & Wang, C. L. (2024) · Industrial Marketing Management · doi:10.1016/j.indmarman.2023.12.018 · Cited by 38

AI in power systems: a systematic review of key matters of concern

Henao, F., Edgell, R., Sharma, A., & Olney, J. R. (2025) · Energy Informatics · doi:10.1186/s42162-025-00529-1 · Cited by 39

Advances and challenges in energy and climate alignment of AI infrastructure expansion

Lal, A., & You, F. (2025) · Advances in Applied Energy · doi:10.1016/j.adapen.2025.100243 · Cited by 35

The ethics of artificial intelligence systems in healthcare and medicine: from a local to a global perspective, and back

Vandemeulebroucke, T. (2024) · Pflügers Archiv - European Journal of Physiology · doi:10.1007/s00424-024-02984-3 · Cited by 40

Exploding AI Power Use: an Opportunity to Rethink Grid Planning and Management

Lin, L., Wijayawardana, R., Rao, V., Nguyen, H. T., Gnibga, W. E., & Chien, A. A. (2024) · ACM International Conference on Future Energy Systems (e-Energy) · doi:10.1145/3632775.3661959 · Cited by 32

A systematic review of electricity demand for large language models: evaluations, challenges, and solutions

Ji, Z., & Jiang, M. F. (2025) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2025.116159 · Cited by 40

Methodology based on spiking neural networks for univariate time-series forecasting

Lucas, S., & Portillo, E. (2024) · Neural Networks · doi:10.1016/j.neunet.2024.106171 · Cited by 30

Providing load flexibility by reshaping power profiles of large language model workloads

Wang, Y., Guo, Q., & Chen, M. (2025) · Advances in Applied Energy · doi:10.1016/j.adapen.2025.100232 · Cited by 30

Engineering Sustainable Data Architectures for Modern Financial Institutions

Ionescu, S.-A., Dıaconıța, V., & Radu, A.-O. (2025) · Electronics · doi:10.3390/electronics14081650 · Cited by 29

Integrating environmental considerations in digital health technology assessment and procurement: Stakeholders’ perspectives

Alami, H., Rivard, L., Lehoux, P., Ahmed, M. A. A., Fortin, J., & Fleet, R. P. (2023) · Digital Health · doi:10.1177/20552076231219113 · Cited by 36

AI-driven cooling technologies for high-performance data centres: state-of-the-art review and future directions

Kez, D. A., Foley, A. M., Wong, F. W. B. M. H., Dolfi, A., & Srinivasan, G. (2025) · Sustainable Energy Technologies and Assessments · doi:10.1016/j.seta.2025.104511 · Cited by 27

Why transparency matters for sustainable data centers and carbon-neutral artificial intelligence (AI)

Hankendi, C., Coskun, A. K., & Sovacool, B. K. (2025) · iScience · doi:10.1016/j.isci.2025.113705 · Cited by 30

Large Language Models on Mobile Devices: Measurements, Analysis, and Insights

Li, X., Lu, Z., Cai, D., Ma, X., & Xu, M. (2024) · doi:10.1145/3662006.3662059 · Cited by 31

Single-Node Power Demand During AI Training: Measurements on an 8-GPU NVIDIA H100 System

Latif, I., Newkirk, A. C., Carbone, M. R., Munir, A., Lin, Y., Koomey, J., Yu, X., & Dong, Z. (2025) · IEEE Access · doi:10.1109/access.2025.3554728 · Cited by 50

Power hungry: How AI will drive energy demand

Bogmans, C., Ganpurev, G., Gomez-Gonzalez, P., Melina, G., Pescatori, A., & Thube, S. (2026) · Energy Economics · doi:10.1016/j.eneco.2026.109278 · Cited by 29

Climate Change and Health: The next Challenge of Ethical AI

Fiske, A., Radhuber, I. M., Willem, T., Buyx, A., Celi, L. A., & McLennan, S. (2025) · The Lancet Global Health · doi:10.1016/s2214-109x(25)00124-x · Cited by 25

A Survey on AI-Driven Energy Optimization in Terrestrial Next Generation Radio Access Networks

Sthankiya, K., Saeed, N. H., McSorley, G., Jaber, M., & Clegg, R. G. (2024) · IEEE Access · doi:10.1109/access.2024.3482561 · Cited by 25

Physics language and language use in physics—What do we know and how AI might enhance language-related research and instruction

Wulff, P. (2023) · European Journal of Physics · doi:10.1088/1361-6404/ad0f9c · Cited by 24

Leveraging Generative AI to Elevate Curriculum Design and Pedagogy in Public Health and Health Promotion

Conrad, E. J., & Hall, K. C. (2024) · Pedagogy in Health Promotion · doi:10.1177/23733799241232641 · Cited by 23

Experimental demonstration of coupled learning in elastic networks

Altman, L. E., Stern, M., Liu, A. J., & Durian, D. J. (2024) · Physical Review Applied · doi:10.1103/physrevapplied.22.024053 · Cited by 31

Energy costs of communicating with AI

Dauner, M., & Socher, G. (2025) · Frontiers in Communication · doi:10.3389/fcomm.2025.1572947 · Cited by 25

Assessing the impact of artificial intelligence on the transition to renewable energy? Analysis of U.S. states under policy uncertainty

Fang, Y., Lee, C., & Li, X. (2025) · Renewable Energy · doi:10.1016/j.renene.2025.122969 · Cited by 23

Classical and Quantum Physical Reservoir Computing for Onboard Artificial Intelligence Systems: A Perspective

Abbas, A. H., Abdel-Ghani, H., & Maksymov, I. S. (2024) · Dynamics · doi:10.3390/dynamics4030033 · Cited by 22

There is no such thing as conscious artificial intelligence

Porębski, A., & Figura, J. (2025) · Humanities and Social Sciences Communications · doi:10.1057/s41599-025-05868-8 · Cited by 20

Artificial Intelligence (AI) and Global Justice

Sahebi, S., & Formosa, P. (2024) · Minds and Machines · doi:10.1007/s11023-024-09708-7 · Cited by 18

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

Advancing Neural Networks: Innovations and Impacts on Energy Consumption

Fedorova, A., Jovišić, N., Vallverdú, J., Battistoni, S., Jovičić, M., Medojević, M., Toschev, A. S., Alshanskaia, E., Talanov, M. O., & Erokhin, V. V. (2024) · Advanced Electronic Materials · doi:10.1002/aelm.202400258 · Cited by 20

Geography for AI sustainability and sustainability for GeoAI

Shi, M., Janowicz, K., Verstegen, J. A., Currier, K., Wiedemann, N., Mai, G., Majić, I., Liu, Z., & Zhu, R. (2025) · Cartography and Geographic Information Science · doi:10.1080/15230406.2025.2479796 · Cited by 27

Integrated Photonic Neural Networks for Equalizing Optical Communication Signals: A Review

Silva, L. C. B., Marciano, P. R. N., Pontes, M. J., Monteiro, M. E., André, P. S. D. B., & Segatto, M. E. V. (2025) · Photonics · doi:10.3390/photonics12010039 · Cited by 19

Generative AI exacerbates the climate crisis

Chen, Q., Wang, J., & Lin, J. (2025) · Science · doi:10.1126/science.adt5536 · Cited by 18

Use of artificial intelligence tools by doctoral students: a mixed-methods explanatory-sequential investigation

Akbar, M. N. (2025) · Journal of Further and Higher Education · doi:10.1080/0309877x.2025.2515135 · Cited by 18

Addressing Challenges for the Effective Adoption of Artificial Intelligence in the Energy Sector

Park, C. (2025) · Sustainability · doi:10.3390/su17135764 · Cited by 17

Geopolitical ecologies of cloud capitalism: Territorial restructuring and the making of national computing power in the U.S. and China

Kollar, J., & Stokols, A. (2025) · Environment and Planning A Economy and Space · doi:10.1177/0308518x251369704 · Cited by 17

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

The attribution problem of a seemingly intangible industry

Falk, S., van Wynsberghe, A., & Biber‐Freudenberger, L. (2024) · Environmental Challenges · doi:10.1016/j.envc.2024.101003 · Cited by 31

Energy and Water Dynamics in Data Center Cooling: Insights from a Modeling Study in Hot-Arid Climates

Karimi, L., Shinh, R., Laisure-Pool, C., Green, M., Yacuel, L., Ashby, J., & Hickenbottom, K. L. (2025) · Applied Thermal Engineering · doi:10.1016/j.applthermaleng.2025.126802 · Cited by 20

A Floating‐Gate Photoelectric Synaptic Transistor Utilizing BP/PO x /WSe 2 Heterostructure for Neuromorphic Visual Processing

Zeng, Y., Lv, W., Hou, Z., Huang, W., Yu, Z., Han, Z., Zhan, R., Zeng, T., Luo, Y., Lin, Y., et al. (2025) · Advanced Science · doi:10.1002/advs.202510063 · Cited by 17

Combining direct and indirect free cooling for data centers via transformation into a building-scale heat exchanger

Kargar, S., & Moran, J. L. (2025) · Applied Energy · doi:10.1016/j.apenergy.2025.125973 · Cited by 17

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

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

Broadening the Gains from Generative AI

Brollo, F., Dabla‐Norris, E., de Mooij, R., Garcia-Macia, D., Hanappi, T., Liu, L., & Nguyen, A. (2024) · IMF staff discussion note · doi:10.5089/9798400277177.006 · Cited by 17

Systematic evaluation and review of Germany renewable energy research: A bibliometric study from 2008 to 2023

He, H., Tu, H., Zhang, H., Luo, S., Feei, Z., Yang, X., Li, Y., Yang, C., Wang, J., & Zhao, Z. (2024) · Heliyon · doi:10.1016/j.heliyon.2024.e34933 · Cited by 15

The study of optimal reactive power dispatch in power systems based on further improved membrane search algorithm

Lai, W., Song, Q., Zheng, X., & Chen, H. (2024) · Applied Energy · doi:10.1016/j.apenergy.2024.124433 · Cited by 15

Energy use of AI inference, efficiency pathways, and test-time scaling

Oviedo, F., Kazhamiaka, F., Choukse, E., Kim, A., Luers, A. L., Nakagawa, M., Bianchini, R., & Ferres, J. L. (2026) · Joule · doi:10.1016/j.joule.2026.102430 · Cited by 14

Artificial Intelligence in Healthcare: ChatGPT and Beyond

Hulsen, T. (2024) · AI · doi:10.3390/ai5020028 · Cited by 14

A Compact 48-V-to-Sub-1-V Switching Bus Converter with 4.7-mm Height for Processor Vertical Power Delivery

Zhu, Y., Zou, J., Ellis, N. M., Kudva, S. S., Mosa, M., Gray, C. T., & Pilawa-Podgurski, R. C. N. (2024) · IEEE Energy Conversion Congress and Exposition (ECCE) · doi:10.1109/ecce55643.2024.10861778 · Cited by 14

AI From the South: artificial intelligence in Latin America through the sociotechnical imaginaries of Brazilian tech workers

Seto, K. S. (2025) · Globalizations · doi:10.1080/14747731.2025.2465166 · Cited by 14

Binder Engineering Suppresses Jahn–Teller‐Driven Mn Dissolution and Enables High‐Loading MnO 2 Cathodes for Aqueous Zn‐Ion Batteries

Jang, I., Yoo, G., & An, G. (2026) · Advanced Functional Materials · doi:10.1002/adfm.75881 · Cited by 13

Getting beyond Net Zero dashboards in the information technology sector

Gregg, M., & Strengers, Y. (2024) · Energy Research & Social Science · doi:10.1016/j.erss.2023.103397 · Cited by 13

A novel deep learning approach (Bi-xBcNet-96) considering green AI to discover breast cancer using mammography images

El-Mawla, N. A., Berbar, M. A., El-Fishawy, N. A., & El-Rashidy, M. A. (2024) · Neural Computing and Applications · doi:10.1007/s00521-024-09815-7 · Cited by 17

Reducing Carbon Footprint in AI: A Framework for Sustainable Training of Large Language Models

Iftikhar, S., & Davy, S. (2024) · Lecture notes in networks and systems · doi:10.1007/978-3-031-73110-5_22 · Cited by 14

Brain-like hardware, do we need it?

Borghi, F., Nieus, T. R., Galli, D. E., & Milani, P. (2024) · Frontiers in Neuroscience · doi:10.3389/fnins.2024.1465789 · Cited by 12

Large AI Models and Their Applications: Classification, Limitations, and Potential Solutions

Bi, J., Wang, Z., Yuan, H., Shi, X., Wang, Z., Zhang, J., Zhou, M., & Buyya, R. (2025) · Software Practice and Experience · doi:10.1002/spe.3408 · Cited by 13

Machine Memory Intelligence: Inspired by Human Memory Mechanisms

Zheng, Q., Liu, H., Zhang, X., Yan, C., Cao, X., Gong, T., Liu, Y., Shi, B., Peng, Z., Fan, X., et al. (2025) · Engineering · doi:10.1016/j.eng.2025.01.012 · Cited by 14

Data Centers, Critical Minerals, Energy, and Geopolitics: The Foundations of Artificial Intelligence

Stacciarini, J. H. S., & Gonçalves, R. J. D. A. F. (2025) · Sociedade & natureza · doi:10.14393/sn-v37-2025-77215 · Cited by 26

EcoLogits: Evaluating the Environmental Impacts of Generative AI

Rincé, S., & Banse, A. (2025) · The Journal of Open Source Software · doi:10.21105/joss.07471 · Cited by 26

Will AI’s growth create an explosion of energy consumption?

Kramer, D. (2024) · Physics Today · doi:10.1063/pt.gqbw.iirw · Cited by 12

From Triumph to Uncertainty: The Journey of Software Engineering in the AI Era

Mastropaolo, A., Escobar‐Velásquez, C., & Linares‐Vásquez, M. (2024) · ACM Transactions on Software Engineering and Methodology · doi:10.1145/3709360 · Cited by 23

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

Digitalization, autonomy and the future of energy policy

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