The carbon impact of artificial intelligence

Does the digital economy really help reduce industrial carbon intensity? Empirical evidence from intermediate inputs of digital products in China

Song, W., & Tang, S. (2024) · Environmental Science and Pollution Research · doi:10.1007/s11356-024-34839-w · Cited by 3

Adaptive, Unlabeled and Real-time Approximate-Learning Platform (AURA) for Personalized Epileptic Seizure ForecastingPreprint

Yang, Y., Truong, N. D., Eshraghian, J. K., Nikpour, A., & Kavehei, O. (2021) · medRxiv · doi:10.1101/2021.09.30.21264287 · Cited by 3

Towards Responsible Artificial Intelligence in Medicine: Reflections from the Australian Epilepsy ProjectPreprint

Pedersen, M., Pardoe, H., de Weger, A., Hutchison, D. M., Abbott, D., Verspoor, K. M., & Jackson, G. D. (2023) · doi:10.31219/osf.io/kag75 · Cited by 3

Artificial intelligence: An explainer

Gajjar, D. (2023) · doi:10.58248/pb57 · Cited by 6

Future of Farming: A Review of Artificial Intelligence Applications in Agricultural Production and Processing

Ashe, G., & Mengistu, D. (2025) · Asian Science Bulletin · doi:10.3923/asb.2025.82.91 · Cited by 2

Advancing Green Artificial Intelligence

Ahmad, F., Dwivedi, S., Khan, Z. H., & Mittal, N. (2025) · IGI Global eBooks · doi:10.4018/979-8-3373-0766-4.ch012 · Cited by 2

Global Synergy: AI’s Role in Advancing Climate Collaboration and International Agreements

Pápay, G. P., & Das, S. (2025) · Springer eBooks · doi:10.1007/978-981-96-8015-3_15 · Cited by 2

The True Tragedy of Artificial Intelligence

Oliveira, D. G. D. S. (2025) · Astrolabio: revista internacional de filosofía · doi:10.1344/astrolabio.v1i30.49350 · Cited by 2

An optimizer derived from Halpern’s method for enhanced neural network convergence and reduced carbon emissions

Colao, V., Foglia, K. R., Giordano, A., Ritacco, E., & Spataro, W. (2025) · Journal of Intelligent Information Systems · doi:10.1007/s10844-025-00969-x · Cited by 2

Speech Recognition with Cochlea‐Inspired In‐Sensor Computing

Beoletto, P. H., Milano, G., Ricciardi, C., Bosia, F., & Gliozzi, A. S. (2025) · Advanced Intelligent Systems · doi:10.1002/aisy.202500526 · Cited by 2

Evaluating Time Series Classification Models for Nocturnal Hypoglycemia: From Predictive Performance to Environmental Impact

Lara-Abelenda, F. J., Chushig-Muzo, D., Acosta, C. B., Wägner, A. M., Granja, C., & Soguero-Ruíz, C. (2025) · IEEE Access · doi:10.1109/access.2025.3600917 · Cited by 4

Is Value Cocreation a Privilege? A Call for Collaborative Action in the Marketplace

Ekpo, A. E., & Thornton, L. M. (2025) · Journal of the Association for Consumer Research · doi:10.1086/738474 · Cited by 2

Sharing Computing Power: Enhancing Energy Efficiency and Reducing Carbon Emissions

Sun, J., Fu, Z., Ma, J., Liu, X., & Wang, Y. (2026) · IEEE Energy Sustainability Magazine · doi:10.1109/esm.2025.3627176 · Cited by 2

Artificial intelligence for low-carbon energy and information networks

Ye, J., Zhao, Y., Yu, Y., Li, Y., Ge, X., Gharavi, H., Yang, Y., & Zhang, W. (2026) · Nature Reviews Electrical Engineering · doi:10.1038/s44287-026-00271-0 · Cited by 3

Latest trends and dynamics in green, sustainable and smart biorefineries

Hohrenk-Danzouma, L. L., Fuente-Ballesteros, A., & Zuin, V. G. (2026) · Sustainable Chemistry for Climate Action · doi:10.1016/j.scca.2026.100202 · Cited by 2

Has China's national big data comprehensive pilot zone reduced corporate carbon emissions? A quasi-natural experiment based on double machine learning

Liu, X., Li, Z., Kong, L., Wang, Y., & Gao, Q. (2026) · Energy Economics · doi:10.1016/j.eneco.2026.109473 · Cited by 2

Digitalization: A Carbon Emission Apocalypse In The 4IR

Mwanaumo, E., Jayeola, F. C., Nyimbili, P. H., & Thwala, W. D. (2023) · Zambia ICT Journal · doi:10.33260/zictjournal.v7i1.161 · Cited by 2

A CO‐LIBERATORY FRAMEWORK FOR BIG DATA

Johnson, M. K., & Robertson, R. S. (2023) · Zygon® · doi:10.1111/zygo.12880 · Cited by 2

Green Machine Learning

Bolón‐Canedo, V., Morán‐Fernández, L., Cancela, B., & Alonso‐Betanzos, A. (2023) · European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN) · doi:10.14428/esann/2023.es2023-3 · Cited by 2

Efficient Global-Context driven Volumetric Segmentation of Abdominal Images

Dutta, P., & Mitra, S. (2023) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm58861.2023.10385802 · Cited by 2

The Use of AI in Personalized Marketing: Balancing Benefits and Privacy Concerns

Yadav, M., Kumar, A., & Jha, R. (2024) · International Journal of Advanced Research in Science Communication and Technology · doi:10.48175/ijarsct-17823 · Cited by 2

Cutting emissions through intelligent production in Chinese manufacturing firms: an empirical analysis

Huang, R., Qin, M., & Yao, X. (2024) · Annals of Operations Research · doi:10.1007/s10479-024-06005-5 · Cited by 2

Real-Life Experimentation with Artificial Intelligence

Elizaveta, G., & Evert, S. (2023) · Oxford University Press eBooks · doi:10.1093/law/9780192882486.003.0036 · Cited by 4

Aprendizagem Profunda e Inteligência Artificial Verde: Caminhos para um Futuro mais Sustentável

Ferraro, V. R. G., Gullo, G., Costa, D. D. S., & Moura, P. N. D. S. (2024) · Anais do Workshop de Computação Aplicada à Gestão do Meio Ambiente e Recursos Naturais (WCAMA) · doi:10.5753/wcama.2024.3033 · Cited by 3

Bridging the gap between artificial intelligence and natural intelligence

Zhu, R.-J., Gunasekaran, S., & Eshraghian, J. K. (2024) · Nature Computational Science · doi:10.1038/s43588-024-00677-6 · Cited by 2

A Critical Framing of Data for Development: Historicizing Data Relations and AI

Mussgnug, A. M., & Leonelli, S. (2024) · Development and Change · doi:10.1111/dech.12857 · Cited by 4

Environmental Sustainability of Machine Learning Systems: Reducing the Carbon Impact of Their Lifecycle Process

Martínez‐Fernández, S. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-78386-9_1 · Cited by 2

Study on Power Demand Estimation Methods for Computing Power Infrastructure

Tang, W., & Li, W. (2024) · International Conference on Electronics and Devices, Computational Science (ICEDCS) · doi:10.1109/icedcs64328.2024.00034 · Cited by 2

Geometry of Linear Neural Networks: Equivariance and Invariance Under Permutation Groups

Kohn, K., Sattelberger, A.-L., & Shahverdi, V. (2025) · SIAM Journal on Matrix Analysis and Applications · doi:10.1137/24m166053x · Cited by 2

Making Artificial Intelligence Sustainable for Healthcare

Bastone, A., Nevi, G., Schiavone, F., Bernhard, F., & Dezi, L. (2024) · Journal of Innovation Economics & Management · doi:10.3917/e.jie.pr1.0157 · Cited by 2

Measuring the Environmental Cost in the Evaluation of Metaheuristics

Hernández, P. N., Puris, A., & Pelta, D. A. (2021) · Atlantis studies in uncertainty modelling/Atlantis Studies in Uncertainty Modelling · doi:10.2991/asum.k.210827.028 · Cited by 2

Artificial Intelligence and Large-Scale Threats to Humanity

Tamburrini, G. (2023) · Springer eBooks · doi:10.1007/978-3-031-45304-5_16 · Cited by 2

Editorial 2024: Large language models, artificial intelligence and geomorphology

Lane, S. N. (2024) · Earth Surface Processes and Landforms · doi:10.1002/esp.5773 · Cited by 2

Making Artificial Intelligence Sustainable for Healthcare 1

Bastone, A., Nevi, G., Schiavone, F., Bernhard, F., & Dezi, L. (2023) · Journal of Innovation Economics & Management · doi:10.3917/jie.pr1.0157 · Cited by 1

Artificial Intelligence and the Right to Health

Echeverría, E. S. (2023) · Oxford University Press eBooks · doi:10.1093/law/9780192882486.003.0026 · Cited by 1

Privacy, Political Participation, and Dissent

Katrak, M., & Ishita, C. (2023) · Oxford University Press eBooks · doi:10.1093/law/9780192882486.003.0010 · Cited by 1

The Rights to Privacy and Data Protection and Facial Recognition Technology in the Global North

González, N. M. (2023) · Oxford University Press eBooks · doi:10.1093/law/9780192882486.003.0009 · Cited by 1

Corporation’s greening strategies: Overlooking the negative implications of AI’s contributions?

Dubey, S., & Alam, S. (2024) · SHS Web of Conferences · doi:10.1051/shsconf/202419401007 · Cited by 1

Sobre La Teoría de Actor Red y el papel de la IA en el cambio climático

Gutiérrez, J. L. M. (2024) · European Public & Social Innovation Review · doi:10.31637/epsir-2024-518 · Cited by 1

Recommendations for Implementation of AI: Steps Toward Industry-Wide Adoption

Gaur, A., Pooja, P., Hooda, S., & Saya, L. (2024) · Springer eBooks · doi:10.1007/978-3-031-69433-2_12 · Cited by 1

Machine Learning within Latent Spaces formed by Foundation Models

Tomczyk, B., Angelov, P. P., & Kangin, D. (2024) · IEEE International Conference on Intelligent Systems · doi:10.1109/is61756.2024.10705264 · Cited by 2

Combining supervised learning and local search for the multicommodity capacitated fixed-charge network design problem

Rocca, C. R. L., Cordeau, J., & Frejinger, E. (2024) · Transportation Research Part E Logistics and Transportation Review · doi:10.1016/j.tre.2024.103805 · Cited by 1

Multi-Objective and Multiple Information Source Optimization for Fair & Green Machine Learning

Candelieri, A., Ponti, A., & Archetti, F. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-81241-5_4 · Cited by 1

Moral values for implementation of AI and machine learning in healthcare

Richman, K. A. (2025) · American Journal of Health-System Pharmacy · doi:10.1093/ajhp/zxaf056 · Cited by 1

Real-time Criminal Face Recognition Using Machine Learning

Singla, S., Singla, P., Khurana, A., Rana, S., Sharma, R., & Sharma, A. (2024) · International Conference on Progressive Innovations in Intelligent Systems and Data Science (ICPIDS) · doi:10.1109/icpids65698.2024.00053 · Cited by 1

Motivation or resistance: A multidimensional analysis of quantile network spillovers between smart grids and carbon markets from a digital technology perspective

Qiao, S., Chang, Y., Yang, M., & Dang, Y. J. (2025) · Technology in Society · doi:10.1016/j.techsoc.2025.103053 · Cited by 1

Hardware and Software Influence on EAs Power Consumption

Álvarez, J. D., Arenas, M. G., Sánchez-Venegas, A., López, G. R., de Vega, F. F., & Castillo, P. Á. (2025) · Lecture notes in computer science · doi:10.1007/978-3-032-02725-2_19 · Cited by 1

Evaluating the environmental sustainability of AI in radiology: a systematic review of current practice

Thomson, R., Perdomo-Lampignano, J. A., Fisher, E., Wati, C., Jeyakumar, G., Duncan, S., & Lowe, D. J. (2025) · BMJ Digital Health & AI · doi:10.1136/bmjdhai-2025-000073 · Cited by 1

Automating the static and seismic design of 2-D multistorey reinforced concrete structures by using Monte Carlo Tree Search and Genetic Algorithm

Rossi, L., & Winands, M. H. M. (2025) · AI in Civil Engineering · doi:10.1007/s43503-025-00073-7 · Cited by 1

Industrial Metaverse challenges, opportunities, and the impact on sustainability

Tsakalos, D., Krimpas, G. A., & Mourtzis, D. (2025) · Digital Twin · doi:10.1080/27525783.2025.2600190 · Cited by 1

Reducing the healthcare carbon footprint through fully self-service artificial intelligence-powered screening: A comparative simulation study utilizing data from Shanghai City and Lixin Town, China

Ma, Y., Shi, X., Lu, L., Xu, Y., Zhang, J., Yu, T., Peng, Y., Ke, E., Lin, S., Guo, R., et al. (2025) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2025.147331 · Cited by 3

How Generative AI is Changing Our World: Reflections from a Geographical Perspective

de Weghe, N. V. (2026) · Key challenges in geography · doi:10.1007/978-3-032-05201-8_2 · Cited by 1

Relationship Between Artificial Intelligence and Carbon Footprint

Dere, G. (2026) · Springer eBooks · doi:10.1007/978-3-032-03718-3_2 · Cited by 1

Energy Cost of the AI/ML Workflow in O-RAN

Chou, S., Hribar, J., Bertalanič, B., Mohorčič, M., Λάγκας, Θ., Sarigiannidis, P. G., & Fortuna, C. (2025) · IEEE Conference on Network Function Virtualization and Software Defined Networks (NFV-SDN) · doi:10.1109/nfv-sdn66355.2025.11349371 · Cited by 1

Introduction to Federated Learning for Smart Mobility

Pei, J., Wang, L., & Dai, M. (2026) · SpringerBriefs in computer science · doi:10.1007/978-981-95-6160-5_1 · Cited by 1

Large language models in aquatic risk assessment: research status and future perspectives

Li, Q., Cheng, F., & You, J. (2026) · Environmental and Biogeochemical Processes · doi:10.48130/ebp-0026-0002 · Cited by 1

Daoist elemental actions in the Silicon Archipelago

Wee, H. (2026) · Environment and Planning F · doi:10.1177/26349825251405822 · Cited by 1

Towards sustainable artificial intelligence: a comprehensive review and comparative analysis of deep learning models’ carbon footprint

Garrab, S., Boughriou, S., & BenSassi, M. (2026) · Applied Intelligence · doi:10.1007/s10489-026-07208-y · Cited by 2

Rat-SLAM with neuromorphic computing

Pirozzo, B. M., Fernández-León, J. A., Paula, M. D., Picos, R., & Acosta, G. G. (2026) · Neurocomputing · doi:10.1016/j.neucom.2026.133572 · Cited by 1

Ethical Considerations for Artificial Intelligence Tools in Small Animal Veterinary Medicine

Coghlan, S., & Quinn, T. (2026) · Veterinary Clinics of North America Small Animal Practice · doi:10.1016/j.cvsm.2026.03.001 · Cited by 1

The Challenging Task of Estimating the Carbon Footprint of Digital Services

Laaksonen, S.-M., & Premsankar, G. (2026) · Emerald Group Publishing Limited eBooks · doi:10.1108/978-1-83662-172-020261003 · Cited by 1

Eco-conscious green AI: Reviewing sustainability approaches to minimize environmental impact

Rajath, S., & Vivian, A. V. (2026) · Next Sustainability · doi:10.1016/j.nxsust.2026.100311 · 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

Machines Writing Histories Using Insect Algorithms: Toward an Age of Environmental Historiography for Artificial Intelligence

Hughes‐Warrington, M. (2026) · Environmental History · doi:10.1086/741355 · Cited by 1

Machine learning meets configurational analysis: unveiling multiple pathways to energy-saving innovation in construction firms

Li, D., Yan, H., Ahmed, A. D., & Ma, S. (2026) · Engineering Construction & Architectural Management · doi:10.1108/ecam-09-2025-1548 · Cited by 2

Real Time Carbon Emissions Calculator for Personal Computers

Khandelwal, S., Sharma, N., & De, P. K. (2022) · Lecture notes on data engineering and communications technologies · doi:10.1007/978-981-19-2600-6_46 · Cited by 1

KI-Realitäten/AI Realities

Groß, R., & Jordan, R. (2023) · AI critique · doi:10.1515/9783839466605-001 · Cited by 1

Research on the application and promotion of the carbon neutral concept based on the attention mechanism in football under the end-to-end architecture

Hou, M., Mu, X., & Liu, S. (2023) · Frontiers in Ecology and Evolution · doi:10.3389/fevo.2023.1272707 · Cited by 1

الذكاء الاصطناعي بين الاعتبار الأخلاقي والمقياس القانوني "أين نحن؟"

الخطيب, م. ع. (2023) · The Jordanian Journal of Law and Political Science · doi:10.35682/jjlps.v15i3.572 · Cited by 2

A mesterséges intelligencia szerepe a fenntartható gazdasági döntésekben

Széles, Z., & Szőke, T. M. (2023) · Soproni Egyetem Kiadó eBooks · doi:10.35511/978-963-334-495-8 · Cited by 1

Data Explosion, Algorithm Economy, and the AI Fervidness

Turi, A. N. (2024) · Future of business and finance · doi:10.1007/978-3-031-46189-7_1 · Cited by 1

HalpernSGD: A Halpern-Inspired Optimizer for Accelerated Neural Network Convergence and Reduced Carbon Footprint

Foglia, K. R., Colao, V., & Ritacco, E. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-62700-2_26 · Cited by 1

Artificial Intelligence and Religious Freedom

Temperman, J. (2023) · Oxford University Press eBooks · doi:10.1093/law/9780192882486.003.0004 · Cited by 3

Artificial Intelligence and the Right to Liberty and Security

Golunova, V. (2023) · Oxford University Press eBooks · doi:10.1093/law/9780192882486.003.0003 · Cited by 1

Artificial Intelligence and Human Rights

Prifti, K., Quintavalla, A., & Temperman, J. (2023) · Oxford University Press eBooks · doi:10.1093/law/9780192882486.003.0029 · Cited by 1

Artificial Intelligence and Consumer Protection Rights

Li, S., Schütte, B., & Majewski, L. (2023) · Oxford University Press eBooks · doi:10.1093/law/9780192882486.003.0027 · Cited by 1

Smart Cities, Artificial Intelligence, and Public Law

Ranchordás, S. (2023) · Oxford University Press eBooks · doi:10.1093/law/9780192882486.003.0033 · Cited by 1

Decarbonizing research laboratories? The tensions associated with the commensurability of carbon and how it opens up the boundaries of responsibility attribution

Hardy, A. (2024) · Review of Agricultural Food and Environmental Studies · doi:10.1007/s41130-024-00211-2 · Cited by 1

AI Innovations in Global Health in the Context of Climate Change

Papadopoulou, P., Apostolaki, S., Lytras, M. D., & Konstantinopoulou, S. (2024) · Advances in healthcare information systems and administration book series · doi:10.4018/979-8-3693-4402-6.ch001 · Cited by 1

Toward responsible artificial intelligence in medicine: Reflections from the Australian epilepsy project

Pedersen, M., Pardoe, H., de Weger, A., Hutchison, D. M., Abbott, D. F., Verspoor, K. M., & Jackson, G. D. (2025) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2025.103192 · Cited by 5

Künstliche Intelligenz, Klima und Bildung

Autenrieth, D., & Schluchter, J.-R. (2025) · merz | medien + erziehung · doi:10.21240/merz/2025.2.12 · Cited by 2

Toward Reusing the Numerical Association Rule Mining Models

Fister, I., Iglesias, A., Gálvez, A., & Fister, I. (2021) · Advances in intelligent systems and computing · doi:10.1007/978-3-030-87869-6_19 · Cited by 1

NiaNet: A framework for constructing Autoencoder architectures using nature-inspired algorithms

Pavlič, S., Fister, I., & Karakatič, S. (2022) · Annals of Computer Science and Information Systems · doi:10.15439/2022f192 · Cited by 1

Artificial Intelligence and Gender-Based Discrimination

Lütz, F. (2023) · Oxford University Press eBooks · doi:10.1093/law/9780192882486.003.0014 · Cited by 1

RHNAS: Realizable Hardware and Neural Architecture SearchPreprint

Akhauri, Y., Niranjan, A., Muñoz, J. P., Banerjee, S., Davare, A., Cocchini, P., Sorokin, A. A., Iyer, R., & Jain, N. K. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2106.09180 · Cited by 3

Smaller, Faster, Lower-Power Analog RRAM Matrix Computing Circuits Without Performance CompromisePreprint

Luo, Y., Zuo, P., Wang, S., Sun, Z., & Huang, R. (2023) · doi:10.36227/techrxiv.23058392 · Cited by 1

Towards robust and generalizable representations of extracellular data using contrastive learningPreprint

Vishnubhotla, A., Loh, C., Paninski, L., Srivastava, A., & Hurwitz, C. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.10.30.564831 · Cited by 1

AI-Driven Sustainability in the Energy Sector

Goff, T. L. (2025) · doi:10.1093/9780198945215.003.0027 · Cited by 1

Sustainable AI

Vogelaar, I. (2025) · Oxford University Press eBooks · doi:10.1093/9780198945215.003.0030 · Cited by 1

From Pixels to Progress

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

Bridging the Gap: Consensus-Based Considerations for AI Usefulness in Healthcare

Salwei, M. E., Morse, K., Saria, S., Shah, N. H., Bedoya, A., Beyer, M., del Río, A. M., Chornenky, D., Lin, A. L., Ruparel, S., et al. (2026) · The American Journal of Bioethics · doi:10.1080/15265161.2026.2617850 · Cited by 1

Implementation of a Bespoke, Collaborative-Filtering, Matrix-Factoring Recommender System Based on a Bernoulli Distribution Model for Webel

Benito, D., & Bosco, M. (2021)

Efficient and robust high-dimensional sparse logistic regression via nonlinear primal-dual hybrid gradient algorithmsPreprint

Darbon, J., & Langlois, G. P. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2111.15426

The American Robot: A Cultural History. DustinAbnet. University of Chicago Press, 2020.

Kizer, Z. (2021) · The Journal of American Culture · doi:10.1111/jacc.13290

Clinical Screening Prediction in the Portuguese National Health Service: Data Analysis, Machine Learning Models, Explainability and Meta-Evaluation

Gonçalves, T., Veladas, R., Yang, H., Vieira, R. L. R., Quaresma, P., Infante, P., Pinto, C. S., de Oliveira, J. M., Ferreira, M. C., Morais, J., et al. (2023) · Future Internet · doi:10.3390/fi15010026

Wie Künstliche Intelligenz zur Nachhaltigkeit beitragen kann

Schuseil, F., & Koch, D. (2023) · Nachhaltige Industrie · doi:10.1007/s43462-023-0957-y

Analyzing the PESTEL Factors on ChatGPT and Its Impact on the Population

(2023) · Advances in educational technologies and instructional design book series · doi:10.4018/978-1-6684-9300-7.ch012

TinyNARM: Simplifying Numerical Association Rule Mining for Running on Microcontrollers

Fister, I., Fister, I., Gálvez, A., & Iglesias, A. (2023) · Lecture notes in networks and systems · doi:10.1007/978-3-031-42529-5_12

Forms of the Energy Internet Under Digital Transformation

Sang, J., Tan, P., Wang, J., Song, J., & He, G. (2023) · IEEE/IAS Industrial and Commercial Power System Asia (I&CPS Asia) · doi:10.1109/icpsasia58343.2023.10294479

Synergies and Safeguards

Bednarz, Z., & Zalnieriute, M. (2023) · Cambridge University Press eBooks · doi:10.1017/9781009334297.013

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