The carbon impact of artificial intelligence

Artificial intelligence: A powerful paradigm for scientific research

Xu, Y., Liu, X., Cao, X., Huang, C., Liu, E., Qian, S., Liu, X., Wu, Y., Dong, F., Qiu, C.-W., et al. (2021) · The Innovation · doi:10.1016/j.xinn.2021.100179 · Cited by 1,726

Training Spiking Neural Networks Using Lessons From Deep Learning

Eshraghian, J. K., Ward, M., Neftci, E., Wang, X., Lenz, G., Dwivedi, G., Bennamoun, M., Jeong, D. S., & Lu, W. D. (2023) · Proceedings of the IEEE · doi:10.1109/jproc.2023.3308088 · Cited by 1,106

The AI gambit: leveraging artificial intelligence to combat climate change—opportunities, challenges, and recommendations

Cowls, J., Tsamados, A., Taddeo, M., & Floridi, L. (2023) · AI & SOCIETY · doi:10.1007/s00146-021-01294-x · Cited by 552

The AI Gambit — Leveraging Artificial Intelligence to Combat Climate Change: Opportunities, Challenges, and Recommendations

Cowls, J., Tsamados, A., Taddeo, M., & Floridi, L. (2021) · doi:10.2139/ssrn.3804983 · Cited by 552

A review of green artificial intelligence: Towards a more sustainable future

Bolón-Canedo, V., Morán-Fernández, L., Cancela, B., & Alonso-Betanzos, A. (2024) · Neurocomputing · doi:10.1016/j.neucom.2024.128096 · Cited by 486

A Categorical Archive of ChatGPT Failures

Borji, A. (2023) · doi:10.21203/rs.3.rs-2895792/v1 · Cited by 514

Leveraging Artificial Intelligence in Marketing for Social Good—An Ethical Perspective

Hermann, E. (2021) · Journal of Business Ethics · doi:10.1007/s10551-021-04843-y · Cited by 504

YOLO advances to its genesis: a decadal and comprehensive review of the You Only Look Once (YOLO) series

Sapkota, R., Flores-Calero, M., Qureshi, R., Badjugar, C., Nepal, U., Poulose, A., Zeno, P. J., Vaddevolu, U. B. P., Khan, S., Shoman, M., et al. (2025) · Artificial Intelligence Review · doi:10.1007/s10462-025-11253-3 · Cited by 528

Does artificial intelligence promote energy transition and curb carbon emissions? The role of trade openness

Wang, Q., Zhang, F., Li, R., & Sun, J. (2024) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2024.141298 · Cited by 363

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

Green Artificial Intelligence: Towards an Efficient, Sustainable and Equitable Technology for Smart Cities and Futures

Yigitcanlar, T., Mehmood, R., & Corchado, J. M. (2021) · Sustainability · doi:10.3390/su13168952 · Cited by 319

The Evolutionary Dynamics of the Artificial Intelligence Ecosystem

Jacobides, M. G., Brusoni, S., & Candelon, F. (2021) · Strategy Science · doi:10.1287/stsc.2021.0148 · Cited by 236

eco2AI: Carbon Emissions Tracking of Machine Learning Models as the First Step Towards Sustainable AI

Budennyy, S., Lazarev, V. D., Zakharenko, N. N., Korovin, A. N., Plosskaya, O. A., Dimitrov, D. V., Akhripkin, V. S., Pavlov, I. V., Oseledets, I. V., Barsola, I. S., et al. (2022) · Doklady Mathematics · doi:10.1134/s1064562422060230 · Cited by 249

Variability in Resistive Memories

Roldán, J. B., Miranda, E. A., Maldonado, D., Mikhaylov, A. N., Викторович, А. Н., Dubkov, A. A., Koryazhkina, M. N., González, M. B., Villena, M. A., Poblador, S., et al. (2023) · Advanced Intelligent Systems · doi:10.1002/aisy.202200338 · Cited by 206

Controllable protein design with language models

Ferruz, N., & Höcker, B. (2022) · Nature Machine Intelligence · doi:10.1038/s42256-022-00499-z · Cited by 209

Artificial intelligence in public health: promises, challenges, and an agenda for policy makers and public health institutions

Пантели, Д., Adib, K., Buttigieg, S., Goiana-da-Silva, F., Ladewig, K., Muscat, N. A., Figueras, J., Novillo-Ortiz, D., & McKee, M. (2025) · The Lancet Public Health · doi:10.1016/s2468-2667(25)00036-2 · Cited by 193

Artificial intelligence for carbon emissions using system of systems theory

Gaur, L., Afaq, A., Arora, G. K., & Khan, N. (2023) · Ecological Informatics · doi:10.1016/j.ecoinf.2023.102165 · Cited by 187

Managing with Artificial Intelligence: An Integrative Framework

Hillebrand, L., Raisch, S., & Schad, J. (2025) · Academy of Management Annals · doi:10.5465/annals.2022.0072 · Cited by 209

The Cloud Is Material: On the Environmental Impacts of Computation and Data Storage

Monserrate, S. G. (2022) · MIT Case Studies in Social and Ethical Responsibilities of Computing · doi:10.21428/2c646de5.031d4553 · Cited by 145

Environmental Sustainability and AI in Radiology: A Double-Edged Sword

Doo, F. X., Vosshenrich, J., Cook, T. S., Moy, L. P., Almeida, E. P. R. P., Woolen, S. A., Gichoya, J. W., Heye, T. J., & Hanneman, K. A. (2024) · Radiology · doi:10.1148/radiol.232030 · Cited by 135

Environmental Sustainability and MRI : Challenges, Opportunities, and a Call for Action

Chaban, Y. V., Vosshenrich, J., McKee, H., Gunasekaran, S., Brown, M. J., Atalay, M. K., Heye, T. J., Markl, M., Woolen, S. A., Simonetti, O. P., et al. (2023) · Journal of Magnetic Resonance Imaging · doi:10.1002/jmri.28994 · Cited by 122

Artificial intelligence and work: a critical review of recent research from the social sciences

Deranty, J.-P., & Corbin, T. (2024) · AI & SOCIETY · doi:10.1007/s00146-022-01496-x · Cited by 118

The lifecycle of algorithmic decision-making systems: Organizational choices and ethical challenges

Marabelli, M., Newell, S., & Handunge, V. R. (2021) · The Journal of Strategic Information Systems · doi:10.1016/j.jsis.2021.101683 · Cited by 140

Blind spots in AI ethics

Hagendorff, T. (2021) · AI and Ethics · doi:10.1007/s43681-021-00122-8 · Cited by 133

The impact of AI on carbon emissions: evidence from 66 countries

Zhong, J., Zhong, Y., Han, M., Yang, T., & Zhang, Q. (2024) · Applied Economics · doi:10.1080/00036846.2023.2203461 · Cited by 105

Broadening the perspective for sustainable artificial intelligence: sustainability criteria and indicators for Artificial Intelligence systems

Rohde, F., Wagner, J., Meyer, A., Reinhard, P., Voß, M., Petschow, U., & Mollen, A. (2024) · Current Opinion in Environmental Sustainability · doi:10.1016/j.cosust.2023.101411 · Cited by 151

Preventing the Immense Increase in the Life-Cycle Energy and Carbon Footprints of LLM-Powered Intelligent Chatbots

Jiang, P., Sonne, C., Li, W., You, F., & You, S. (2024) · Engineering · doi:10.1016/j.eng.2024.04.002 · Cited by 133

Impact of digital technology on carbon emissions: Evidence from Chinese cities

Shen, Y., Yang, Z., & Zhang, X. (2023) · Frontiers in Ecology and Evolution · doi:10.3389/fevo.2023.1166376 · Cited by 115

Artificial intelligence and climate change: ethical issues

Nordgren, A. (2023) · Journal of Information, Communication and Ethics in Society · doi:10.1108/jices-11-2021-0106 · Cited by 94

Culture, energy and climate sustainability, and smart home technologies: A mixed methods comparison of four countries

Rio, D. D. F. D., Sovacool, B. K., & Griffiths, S. (2021) · Energy and Climate Change · doi:10.1016/j.egycc.2021.100035 · Cited by 105

Memristor-Based Binarized Spiking Neural Networks: Challenges and applications

Eshraghian, J. K., Wang, X., & Lu, W. D. (2022) · IEEE Nanotechnology Magazine · doi:10.1109/mnano.2022.3141443 · Cited by 87

Speciesist bias in AI: how AI applications perpetuate discrimination and unfair outcomes against animals

Hagendorff, T., Bossert, L. N., Tse, Y. F., & Singer, P. (2022) · AI and Ethics · doi:10.1007/s43681-022-00199-9 · Cited by 102

Large language model applications for evaluation: Opportunities and ethical implications

Head, C. B., Jasper, P., McConnachie, M. M., Raftree, L., & Higdon, G. L. (2023) · New Directions for Evaluation · doi:10.1002/ev.20556 · Cited by 97

To image, or not to image: class-specific diffractive cameras with all-optical erasure of undesired objects

Bai, B., Luo, Y., Gan, T., Hu, J., Li, Y., Zhao, Y., Mengü, D., Jarrahi, M., & Özcan, A. (2022) · eLight · doi:10.1186/s43593-022-00021-3 · Cited by 85

Soybean disease identification using original field images and transfer learning with convolutional neural networks

Bevers, N., Sikora, E. J., & Hardy, N. B. (2022) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2022.107449 · Cited by 84

Artificial intelligence enabled carbon capture: A review

Priya, A. K., Balaji, D., Alagumalai, A., & Song, H. (2023) · The Science of The Total Environment · doi:10.1016/j.scitotenv.2023.163913 · Cited by 79

AI and the governance of sustainable development. An idea analysis of the European Union, the United Nations, and the World Economic Forum

Francisco, M., & Linnér, B. (2023) · Environmental Science & Policy · doi:10.1016/j.envsci.2023.103590 · Cited by 108

The carbon emissions of writing and illustrating are lower for AI than for humans

Tomlinson, B., Black, R. W., Patterson, D. J., & Torrance, A. W. (2024) · Scientific Reports · doi:10.1038/s41598-024-54271-x · Cited by 77

Assessing the current landscape of AI and sustainability literature: identifying key trends, addressing gaps and challenges

Tripathi, S., Bachmann, N., Brunner, M., Rizk, Z., & Jodlbauer, H. (2024) · Journal Of Big Data · doi:10.1186/s40537-024-00912-x · Cited by 114

Sustainability implications of artificial intelligence in the chemical industry: A conceptual framework

Liao, M., Lan, K., & Yao, Y. (2021) · Journal of Industrial Ecology · doi:10.1111/jiec.13214 · Cited by 84

Nanoelectronics Using Metal–Insulator Transition

Lee, Y. J., Kim, Y., Gim, H., Hong, K., & Jang, H. W. (2023) · Advanced Materials · doi:10.1002/adma.202305353 · Cited by 77

The Potential of Machine Learning for Enhancing CO2 Sequestration, Storage, Transportation, and Utilization-based Processes: A Brief Perspective

Gupta, S., & Li, L. (2022) · JOM · doi:10.1007/s11837-021-05079-x · Cited by 73

Artificial intelligence and carbon footprints: Roadmap for Indian agriculture

Mor, S., Madan, S., & Prasad, K. D. (2021) · Strategic Change · doi:10.1002/jsc.2409 · Cited by 71

Artificial Intelligence Impact on the Environment: Hidden Ecological Costs and Ethical-Legal Issues

Zhuk, A. (2023) · Journal of Digital Technologies and Law · doi:10.21202/jdtl.2023.40 · Cited by 88

Use of AI in Cardiac CT and MRI: A Scientific Statement from the ESCR, EuSoMII, NASCI, SCCT, SCMR, SIIM, and RSNA

Mastrodicasa, D., van Assen, M., Huisman, M., Leiner, T., Williamson, E. E., Nicol, E. D., Allen, B. D., Saba, L., Vliegenthart, R., & Hanneman, K. (2025) · Radiology · doi:10.1148/radiol.240516 · Cited by 69

Reconciling the contrasting narratives on the environmental impact of large language models

Ren, S., Tomlinson, B., Black, R. W., & Torrance, A. W. (2024) · Scientific Reports · doi:10.1038/s41598-024-76682-6 · Cited by 68

Potential reduction in healthcare carbon footprint by autonomous artificial intelligence

Wolf, R. M., Abramoff, M. D., Channa, R., Tava, C., Clarida, W., & Lehmann, H. P. (2022) · npj Digital Medicine · doi:10.1038/s41746-022-00605-w · Cited by 63

An Outlook for Deep Learning in Ecosystem Science

Perry, G. L. W., Seidl, R., Bellvé, A. M., & Rammer, W. (2022) · Ecosystems · doi:10.1007/s10021-022-00789-y · Cited by 72

Creative Learning for Sustainability in a World of AI: Action, Mindset, Values

Henriksen, D., Mishra, P., & Stern, R. (2024) · Sustainability · doi:10.3390/su16114451 · Cited by 74

Artificial Intelligence in the Provision of Health Care: An American College of Physicians Policy Position Paper

Daneshvar, N., Pandita, D., Erickson, S. M., Sulmasy, L. S., & DeCamp, M. W. (2024) · Annals of Internal Medicine · doi:10.7326/m24-0146 · Cited by 69

Small and medium-sized enterprises as technology innovation intermediaries in sustainable business ecosystem: interplay between AI adoption, low carbon management and resilience

Roux, M., Chowdhury, S., Dey, P. K., Yaroson, E. V., Pereira, V. E., & Abadie, A. (2023) · Annals of Operations Research · doi:10.1007/s10479-023-05760-1 · Cited by 77

Medical Ethics of Large Language Models in Medicine

Ong, J. C. L., Chang, Y., Wasswa, W., Butte, A. J., Shah, N. H., Chew, L. S. T., Liu, N., Doshi‐Velez, F., Lu, W., Savulescu, J., et al. (2024) · NEJM AI · doi:10.1056/aira2400038 · Cited by 62

Guidelines and standard frameworks for artificial intelligence in medicine: a systematic review

Shiferaw, K. B., Roloff, M., Balaur, I., Welter, D., Waltemath, D., & Zeleke, A. A. (2024) · JAMIA Open · doi:10.1093/jamiaopen/ooae155 · Cited by 57

Digital data‐driven technologies and the environmental sustainability of micro, small, and medium enterprises: Does size matter?

Hernández, V., Revilla, A. J., & Rodríguez, A. (2024) · Business Strategy and the Environment · doi:10.1002/bse.3765 · Cited by 53

Moisture content monitoring in industrial-scale composting systems using low-cost sensor-based machine learning techniques

Moncks, P. C. S., Corrêa, É. K., Guidoni, L. L. C., Moncks, R. B., Corrêa, L. B., Lucia, T., Araujo, R. M., Yamin, A. C., & Marques, F. S. (2022) · Bioresource Technology · doi:10.1016/j.biortech.2022.127456 · Cited by 52

Reporting electricity consumption is essential for sustainable AI

Debus, C., Piraud, M., Streit, A., Theis, F. J., & Götz, M. (2023) · Nature Machine Intelligence · doi:10.1038/s42256-023-00750-1 · Cited by 60

The Two Tales of AI: A Global assessment of the environmental impacts of artificial intelligence from a multidimensional policy perspective.

Alnafrah, I. (2025) · Journal of Environmental Management · doi:10.1016/j.jenvman.2025.126813 · Cited by 56

Other’s shoes also fit well: AI technologies contribute to China’s blue skies as well as carbon reduction

Chu, Z., Chen, P., Zhang, Z., & Chen, Z. (2024) · Journal of Environmental Management · doi:10.1016/j.jenvman.2024.120171 · Cited by 51

How can AI reduce carbon emissions? Insights from a quasi-natural experiment using generalized random forest

Feng, L., Qi, J., & Zheng, Y. (2024) · Energy Economics · doi:10.1016/j.eneco.2024.108040 · Cited by 51

Greening the Artificial Intelligence for a Sustainable Planet: An Editorial Commentary

Yiğitcanlar, T. (2021) · Sustainability · doi:10.3390/su132413508 · Cited by 56

Application of artificial intelligence in environmental sustainability and climate change

Kumari, N., & Pandey, S. (2022) · Elsevier eBooks · doi:10.1016/b978-0-323-99714-0.00018-2 · Cited by 49

Electrochemical Random-Access Memory: Progress, Perspectives, and Opportunities

Talin, A. A., Meyer, J. A., Li, J., Huang, M., Schwacke, M., Chung, H. W., Xu, L., Fuller, E. J., Li, Y., & Yildiz, B. (2025) · Chemical Reviews · doi:10.1021/acs.chemrev.4c00512 · Cited by 51

Sustainable artificial intelligence: A corporate culture perspective

Isensee, C., Griese, K., & Teuteberg, F. (2021) · NachhaltigkeitsManagementForum | Sustainability Management Forum · doi:10.1007/s00550-021-00524-6 · Cited by 44

Intelligent Photonics: A Disruptive Technology to Shape the Present and Redefine the Future

Xu, D., Ma, Y., Jin, G., & Cao, L. (2024) · Engineering · doi:10.1016/j.eng.2024.08.016 · Cited by 47

ASTROMER

Donoso-Oliva, C., Becker, I., Protopapas, P., Cabrera-Vives, G., Vishnu, M., & Vardhan, H. (2022) · Astronomy and Astrophysics · doi:10.1051/0004-6361/202243928 · Cited by 60

Global Convergence of MAML and Theory-Inspired Neural Architecture Search for Few-Shot Learning

Wang, H., Wang, Y., Sun, R., & Li, B. (2022) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52688.2022.00957 · Cited by 42

An AI-enabled approach for improving advertising identification and promotion in social networks

Shi, B., & Wang, H. (2022) · Technological Forecasting and Social Change · doi:10.1016/j.techfore.2022.122269 · Cited by 40

Can digital technology reduce carbon emissions? Evidence from Chinese cities

Liu, H., Wang, L., & Shen, Y. (2023) · Frontiers in Ecology and Evolution · doi:10.3389/fevo.2023.1205634 · Cited by 41

Comparative Analysis of Generative AI Risks in the Public Sector

Navarro, M. A. B., Mondragón, M. I. R., & Han, S. H. (2024) · Annual International Conference on Digital Government Research · doi:10.1145/3657054.3657125 · Cited by 48

A Gentle Introduction to Bayesian Optimization

Candelieri, A. (2021) · Winter Simulation Conference · doi:10.1109/wsc52266.2021.9715413 · Cited by 46

Measuring the environmental impacts of artificial intelligence compute and applications

OECD (2022) · OECD digital economy papers · doi:10.1787/7babf571-en · Cited by 96

Fair and green hyperparameter optimization via multi-objective and multiple information source Bayesian optimization

Candelieri, A., Ponti, A., & Archetti, F. (2024) · Machine Learning · doi:10.1007/s10994-024-06515-0 · Cited by 48

FedZero: Leveraging Renewable Excess Energy in Federated Learning

Wiesner, P., Khalili, R., Grinwald, D., Agrawal, P., Thamsen, L., & Kao, O. (2024) · ACM International Conference on Future Energy Systems (e-Energy) · doi:10.1145/3632775.3639589 · Cited by 44

High-speed, low-voltage, low-bit-energy silicon photonic crystal slow-light modulator with impedance-engineered distributed electrodes

Kawahara, K., Tsuchizawa, T., Yamamoto, N., Maegami, Y., Yamada, K., Hara, S., & Baba, T. (2024) · Optica · doi:10.1364/optica.531372 · Cited by 34

Efficient adaptive ensembling for image classification

Bruno, A., Moroni, D., & Martinelli, M. (2023) · Expert Systems · doi:10.1111/exsy.13424 · Cited by 44

Controversies, contradiction, and “participation” in AI

Sloane, M. (2024) · Big Data & Society · doi:10.1177/20539517241235862 · Cited by 43

FreeREA: Training-Free Evolution-based Architecture Search

Cavagnero, N., Robbiano, L., Barbara Caputo, B., & Averta, G. (2023) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv56688.2023.00154 · Cited by 35

Artificial intelligence: a new field of knowledge for nephrologists?

de Arizón, L. F., Ramírez, E. R. V., Teran, M. P., Perera-Lluna, A., de Maeztu, G., Nicolau, A., Furlano, M. M., & Torrá, R. (2023) · Clinical Kidney Journal · doi:10.1093/ckj/sfad182 · Cited by 35

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

CCVS: Context-aware Controllable Video SynthesisPreprint

Moing, G. L., Ponce, J., & Schmid, C. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2107.08037 · Cited by 34

The Effects of Artificial Intelligence on the Fashion Industry—Opportunities and Challenges for Sustainable Transformation

Bieńkowska, J. (2024) · Sustainable Development · doi:10.1002/sd.3312 · Cited by 32

Role of artificial intelligence in environmental sustainability

Habila, M. A., Ouladsmane, M., & ALOthman, Z. A. (2022) · Elsevier eBooks · doi:10.1016/b978-0-323-99714-0.00009-1 · Cited by 32

How are artificial intelligence, carbon market, and energy sector connected? A systematic analysis of time-frequency spillovers

Xu, Y., Shao, X., & Tănăsescu, C. R. (2024) · Energy Economics · doi:10.1016/j.eneco.2024.107477 · Cited by 35

Imagining the future of artificial intelligence in education: a review of social science fiction

Gidiotis, I., & Hrastinski, S. (2024) · Learning Media and Technology · doi:10.1080/17439884.2024.2365829 · Cited by 31

Chasing Green: The Synergistic Effect of Industrial Intelligence on Pollution Control and Carbon Reduction and Its Mechanisms

Shen, Y., & Yang, Z. (2023) · Sustainability · doi:10.3390/su15086401 · Cited by 32

May Artificial Intelligence take health and sustainability on a honeymoon? Towards green technologies for multidimensional health and environmental justice

Moyano-Fernández, C., Rueda, J., Delgado, J., & Ausín, T. (2024) · Global Bioethics · doi:10.1080/11287462.2024.2322208 · Cited by 30

Performance enhancement of artificial intelligence: A survey

Krichen, M., & Abdalzaher, M. S. (2024) · Journal of Network and Computer Applications · doi:10.1016/j.jnca.2024.104034 · Cited by 35

Epistemic Rights and Responsibilities of Digital Simulacra for Biomedicine

Cho, M. K., & Martinez‐Martin, N. (2022) · The American Journal of Bioethics · doi:10.1080/15265161.2022.2146785 · Cited by 32

Comparative analysis of model compression techniques for achieving carbon efficient AI

Paula, E., Soni, J., Upadhyay, H., & Lagos, L. E. (2025) · Scientific Reports · doi:10.1038/s41598-025-07821-w · Cited by 38

Automated in-season rice crop mapping using Sentinel time-series data and Google Earth Engine: A case study in climate-risk prone Bangladesh

Tiwari, V., Tulbure, M. G., Caineta, J., Gaines, M. D., Perin, V., Kamal, M., Krupnik, T. J., Aziz, M. A., & Islam, A. T. (2023) · Journal of Environmental Management · doi:10.1016/j.jenvman.2023.119615 · Cited by 28

The asymmetric impacts of artificial intelligence and oil shocks on clean energy industries by considering COVID-19

Zhang, H., Fang, B., He, P., & Gao, W. (2024) · Energy · doi:10.1016/j.energy.2023.130197 · Cited by 30

How does artificial intelligence change carbon emission intensity? A firm lifecycle perspective

Wu, Q., & Zhou, P. (2025) · Applied Economics · doi:10.1080/00036846.2025.2482927 · Cited by 28

Artificial Intelligence in Ecology: A Commentary on a Chatbot's Perspective

Haghighi, S. R., Saqalaksari, M. P., & Johnson, S. N. (2023) · Bulletin of the Ecological Society of America · doi:10.1002/bes2.2097 · Cited by 42

CE-BERT: Concise and Efficient BERT-Based Model for Detecting Rumors on Twitter

Anggrainingsih, R., Hassan, G. M., & Datta, A. (2023) · IEEE Access · doi:10.1109/access.2023.3299858 · Cited by 32

The ethics of sustainable AI : Why animals (should) matter for a sustainable use of AI

Bossert, L., & Hagendorff, T. (2023) · Sustainable Development · doi:10.1002/sd.2596 · Cited by 19

Improving Automated Machine-Learning Systems through Green AI

Castellanos–Nieves, D., & García-Forte, L. (2023) · Applied Sciences · doi:10.3390/app132011583 · Cited by 34

Significant Applications of Artificial Intelligence Towards Attaining Sustainability

Haleem, A., Javaid, M., Khan, I. H., & Mohan, S. K. (2023) · Journal of Industrial Integration and Management · doi:10.1142/s2424862223500331 · Cited by 35

Decolonial AI as Disenclosure

Mollema, W. J. T. (2024) · Open Journal of Social Sciences · doi:10.4236/jss.2024.122032 · Cited by 26

Two-Speed Deep-Learning Ensemble for Classification of Incremental Land-Cover Satellite Image Patches

Horry, M. J., Chakraborty, S., Pradhan, B., Shulka, N., & Almazroui, M. (2023) · Earth Systems and Environment · doi:10.1007/s41748-023-00343-3 · Cited by 28

Optimal Large Language Model Characteristics to Balance Accuracy and Energy Use for Sustainable Medical Applications

Doo, F. X., Savani, D., Kanhere, A., Carlos, R. C., Joshi, A., Yi, P. H., & Parekh, V. S. (2024) · Radiology · doi:10.1148/radiol.240320 · Cited by 25

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

Gosaliya, U., Dasalaniya, M., Jani, A., & Gajjar, V. (2026) · INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT · doi:10.55041/ijsrem61165

Artificial Intelligence—Reducing the Carbon Footprint?

Shumskaia, E. I. (2022) · Industry 4.0 · doi:10.1007/978-3-030-79496-5_33 · Cited by 9

Estimating Carbon Emissions of Artificial Intelligence [Opinion]

Luccioni, A. S., Lacoste, A., & Schmidt, V. M. (2020) · IEEE Technology and Society Magazine · doi:10.1109/mts.2020.2991496 · Cited by 29