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

Emergent Photoneuromorphic Properties Driven by Defect‐Mediated Trap States in Ternary Copper Chalcogenide Nanocrystals

Roy, A., Karaba, V., Roy, U., Karmakar, P., & Santra, P. K. (2026) · Advanced Functional Materials · doi:10.1002/adfm.77149

Out-of-Plane NbOx oscillators with tunable frequencies and bursting dynamics on millisecond timescalesPreprint

Sirjita, E.-N., Boulle, A., Orlianges, J.-C., Mayet, R., & Crunteanu, A. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.7173729

Informationstechnologie für Nachhaltigkeit: Die versteckten Kosten des digitalen Zeitalters

Lemke, C. (2026) · Springer eBooks · doi:10.1007/978-3-032-29607-8_9

A bi-level planning model of integrated energy capacity configuration for data center cluster considering computing-electricity coupling operation and supply-demand uncertainties

Zhang, S., Ding, S., Li, Y., Wei, M., & Tan, Q. (2026) · Applied Energy · doi:10.1016/j.apenergy.2026.128499

Digitalization and Corporate Environmental Sustainability in an Ethical Context: Mapping the Landscape Through Meta-Analysis

Xie, X., & Han, Y. (2026) · Journal of Business Ethics · doi:10.1007/s10551-026-06414-5

Nachhaltige Digitalisierung, digitale Nachhaltigkeit und digitale Verantwortung

Bieser, J. C. T., Stuermer, M., & Obwegeser, N. (2026) · Springer eBooks · doi:10.1007/978-3-662-72977-9_4

The Role of AI and Emerging Technologies in Enhancing Energy Security and Resilience in the Arabian Gulf

Shakra, R. B. (2026) · Advances in Science, Technology & Innovation/Advances in science, technology & innovation · doi:10.1007/978-3-032-19354-4_27

Toward Sustainable On-Device Intelligence: A Survey on Energy-Efficient RAG Systems with Small Language ModelsPreprint

Cheng, Z., Lai, L., Liu, Y., & Sun, Y. (2026) · Research Square · doi:10.2139/ssrn.6698538

Rite to mine: Bitcoin, ritual, and oligarchy

Marshall, O. (2026) · Science as Culture · doi:10.1080/09505431.2026.2710284

Engineered interfaces in electronic materials for energy-efficient computing

Iyengar, S. A., Ajayan, P. M., Meunier, V., Ghani, T., Salahuddin, S., & Khan, A. I. (2026) · Nature Reviews Materials · doi:10.1038/s41578-026-00949-9

Engineering and Public Policy

Robak, A. M., & McLaughlin, J. D. (2026) · International Conference on Advanced Composite Materials in Bridges and Structures (ACMBS) · doi:10.3138/csce-2025-0121

“Back to the Communities”: A Participatory Evaluation of Cultural Sensitivity in Text-to-Image ModelsPreprint

Kiden, S., Peter, O., Reyes-Cruz, G., Klyshbekova, M., Choi, S., Bergin, A., Waheed, M., Eke, D., Azim, T., Ramchurn, S. D., et al. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.7228948

KI-Lernen: das kostet Energie … !?

Friederichs, H. (2026) · doi:10.59350/jgh8f-c8q05

KI-Lernen: das kostet Energie … !?

Friederichs, H. (2026) · doi:10.59350/h9j9p-kcn04

The E-Waste Crisis Behind Big Tech

Selvakumar, P. (2026) · IGI Global eBooks · doi:10.4018/979-8-3373-8490-0.ch006

An Initial Investigation on the Generation of Logic Gate Networks using Cartesian Genetic Programming

Lubrano, F., Messetti, E., Scionti, A., Squillero, G., & Tonda, A. P. (2026) · Genetic and Evolutionary Computation Conference Companion (GECCO Companion) · doi:10.1145/3795101.3814731

Coordinating electricity and computility in economic dispatch by embedding quantile convex latency surrogates

Sang, L., Liu, J., Qian, T., Zhao, H., Huang, C., Hu, Q., & Wu, Z. (2026) · Applied Energy · doi:10.1016/j.apenergy.2026.128678

Understanding Power Limiting Mechanisms in Modern Processors: A Deep Dive Into Intel RAPL and Turbo Boost Dynamics

Menra, R., Rosinosky, G., Koutsiamanis, R.-A., Bolle, S., & Menaud, J.-M. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-35251-4_10

Powering AI Data Centres in Grid-Constrained Economies: A Techno-Economic Assessment of Renewable Energy Supply Pathways in South AfricaPreprint

Yakubu, R. O., Nel, H., & Thopil, G. A. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.7291444

Talk is cheap, green is hard: The effect of AI-washing on corporate energy transition

Wang, S., Wang, Y., Gao, Y., & Lyu, X. (2026) · International Review of Economics & Finance · doi:10.1016/j.iref.2026.105756

High-precision integrated diffractive optical networks enabling regression analysis

Chen, C., Ding, H., Yu, Y., & Zhang, X. (2026) · Nature Communications · doi:10.1038/s41467-026-76933-2

China’sArtificial Intelligence Industry as a Carbon-Linked Production System: Embodied Emissions, Structural Paths and Demand-Side Drivers

Chen, M., Li, G., Yan, L., Meng, W., & Li, W. (2026) · Systems · doi:10.3390/systems14081000

Coordinated Operation of Data Center Aggregations in Modern Distribution Networks

Arkorful, I. P. K., Ofori, C., Boateng, M., Addo, P. A., Sam-Okyere, Y. A., & Dasori, E. A. (2026) · International Conference on Cybersecurity, Digital Forensics, and AI Applications (ICCSDFAI) · doi:10.1109/iccsdfai70505.2026.11647872

Performance Analysis of a Modular Framework for Edge-Based Generative Conversational AI

Mazzone, L., & Pau, D. P. (2026) · Applied Sciences · doi:10.3390/app16168157

Green AI for Sustainable Computing: Reducing Model Size, Power Consumption, and Computational Cost in Neural Networks through Pruning and Quantization

Rathore, S. P. S., Praveen, T., Sumalatha, V., Lincy, R. B., Mohan, N., & Kumar, J. D. (2026) · International Conference on Sustainable Computing and Integrated Communication in Changing Landscape of AI (ICSCAI) · doi:10.1109/icscai68849.2026.11648559

The Dark Sides of Synthetic and Automated Life

Siibak, A., & Mascheroni, G. (2026) · Studies in childhood and youth · doi:10.1007/978-981-95-8922-7_3

DVFS-Aware Energy-Efficient Implementation of DNNs on Heterogeneous Hybrid Parallel Computers

Khaleghzadeh, H., Khazaei, A., & Lastovetsky, A. (2026) · IEEE Access · doi:10.1109/access.2026.3725140

Synergistic relaxor regulation and 0-3 perovskite-pyrochlore composite design for high-performance energy storage in BiFeO3-BaTiO3 ceramics

Zhang, H., Shui, A., Peng, C., Yu, H., & Zhong, X. (2026) · Ceramics International · doi:10.1016/j.ceramint.2026.08.300

Digital technology innovation and carbon productivity in China's low-carbon transition: Evidence from provincial data

Huang, W., Wang, Y., & Ren, Y. (2026) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2026.149230

The Entropy of Artificial Cognition: Deciphering the Anthropogenic Footprint and Multilingual Energetic Tax of Large Language Models

Anaswara, K. S., & Krishna Mukundan, M. (2026) · International Journal of Technology & Emerging Research · doi:10.64823/ijter.2621020

Credible Sovereignty: Operationalizing AI Governance Across Infrastructure, Data, and Models: A Systematic Review

Raghu Raman, R., & Nedungadi, P. (2026) · AI · doi:10.3390/ai7090327 · Cited by 1

Source-Gated Transistors as BEOL-Compatible Devices for Monolithic 3D Integration: Architectures, Materials, and Spatial Validation

우소정, Kim, H., Lee, S., Lim, S.-C., & Kim, J. (2026) · Electronics · doi:10.3390/electronics15173824

The Energy and Carbon Footprint of Generative Artificial Intelligence: A Physics-Based Assessment of High-Performance Computing SystemsPreprint

徐, 蕾. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.7357562

Certified envelopes for day-ahead cooling-side flexibility from liquid-cooled AI data-center thermal inertia in electricity marketsPreprint

Shen, J., & Zhong, H. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.7372622

Planning smart energy systems for the AI era: A multi-criteria comparison of solar, wind, hydrogen and nuclear pathwaysPreprint

Trušiņa, I., Абрамов, В. И., Gopejenko, V. I., & Mukhamediev, R. I. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.7382266

Interhalogen Complexation Suppresses Hydrolysis–Corrosion Cross‐Talk Degradation in Aqueous Two‐Electron Zn–I 2 Batteries

Kim, H., Jeong, S. W., Jo, D. H., Byun, J., Joo, E. J., Na, B., Kim, H., Ji, X., Lee, S., & Lee, K. T. (2026) · Small · doi:10.1002/smll.75496

Data-center expansion and the dynamic regional redistribution of the water-energy-food stress in Texas

Albatayneh, R. (2026) · Resources Conservation and Recycling · doi:10.1016/j.resconrec.2026.109144

Generative AI's Energy Footprint: Estimating Demand, Confronting Supply Constraints, and Charting a Path to Responsible ScalingPreprint

Maruvada, R. S., & Pulapaka, R. D. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.7404754

EU Influence in Global AI Governance and its Limits

Hoch, S., & Mügge, D. (2026) · Digital Society · doi:10.1007/s44206-026-00284-0

Resource Management Challenges in AI-Driven Data Centers: A Systematic Review of Energy, Water, and Material Constraints

Posadas-Paredes, T., Zavala-Vega, D. K., Márquez, C. R., & Ponce‐Ortega, J. M. (2026) · Resources · doi:10.3390/resources15090115

Data centers in integrated energy systems: A comprehensive review of sustainability evaluation, multi-energy coupling, and cross-timescale flexibility

Wang, X. P., Zhu, H., Zhu, J., Dong, Z. Y., Yang, X., & Li, J. (2026) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2026.117460

Core open science practices in major medical journals: development of an automatized tool based on a Large Language Model

Vinatier, C., Freyermuth, G., Millour, M., Lyan, G. L. B., Buet, S., Lunel, P., Robillard, N., Naudet, F., & Acher, M. (2026) · Open Research Europe · doi:10.12688/openreseurope.24615.1

Beyond ChatGPT: A Roadmap for Agentic AI in Mineral Processing

Chelgani, S. C. (2026) · Mineral Processing and Extractive Metallurgy Review · doi:10.1080/08827508.2026.2731556

AI in sustainable development management for international trade: opportunities, threats and the growth paradox

Brzyska, J., & Ruta-Kujawa, M. (2026) · Economics and Environment · doi:10.34659/eis.2026.98.3.1326

A Multiscale Modeling Framework for Ammonia Heat Transformers: Upgrading Low-Grade Waste Heat from Data CentersPreprint

Yeh, C.-Y., Martínez, A., Gupta, H., Dehgahi, S., Pozarlik, A. K., Jafari, D., Faria, J., & Singh, A. K. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.7442151

Data-driven boiling heat transfer performance prediction using vision-based feature modeling

Lee, J. H., Park, H., Lee, D., & Lee, N. (2026) · International Communications in Heat and Mass Transfer · doi:10.1016/j.icheatmasstransfer.2026.112608

Environmental Impact of Cloud-Hosted Large Language Models

Khan, A. F., O'Connor, D., Luttman, D., & Tan, Q. (2026) · International Conference on Cloud Computing and Big Data Analytics (ICCCBDA) · doi:10.1109/icccbda69573.2026.11676514

Janus 2D Ag$$_{2}$$CdP$$_{2}$$S$$_{3}$$X$$_{3}$$ ($$\textit{X} = \text {O}, \text {Se}, \text {Te}$$) Monolayers as High-Performance Photoelectrodes for Solar-Driven Green Hydrogen Generation: A First-Principles Perspective

Chafai, A. (2026) · The European Physical Journal Plus · doi:10.1140/epjp/s13360-026-08267-3

Urban electricity demand in response to price surges: Evidence from three U.S. cities

Song, L., Fan, P., Yu, S., Li, L., You, P., & Zhou, Y. (2026) · Applied Energy · doi:10.1016/j.apenergy.2026.128810

Green-AI for Eco-Intelligence with Renewable Energy Resources and Climate Actions

Abinaya., M., Jeevithaa., S., Harshmitha., A., & Maha Vidhya., C. (2026) · International Conference on Modern Sustainable Systems (CMSS) · doi:10.1109/cmss69636.2026.11689462

Digitalisation and the Renewable‐Energy Share: Evidence From 156 Countries, 2010–2022

Cong, L., Lu, S., Jiang, P., & Lü, X. (2026) · Sustainable Development · doi:10.1002/sd.71697

Frontier-AI Salience and Electricity Generation in China: Open-Data Evidence for Sustainable Energy MonitoringPreprint

Celebi, O., & Usupbeyli, A. (2026) · Research Square · doi:10.21203/rs.3.rs-9873951/v1

Learning to Compress: Energy-Aware Large Language Model Serving via Adaptive KV Caching

Cheng, Y., Shao, Z., Yu, N., Fu, Y., & Dong, Y. (2026) · IEEE Power Engineering Society General Meeting · doi:10.1109/pesgm58988.2026.11693290

Suppression of skin-effect-enhanced magnetic viscosity for ultralow-loss MHz soft magnetic composites

Yang, S., Fan, X., Hou, C., Jiang, W., Cao, J., Wang, T., Qiao, L., & Li, F. (2026) · Acta Materialia · doi:10.1016/j.actamat.2026.122750

Operation control for green data centers supplied by two-terminal voltage source converter with bipolar DC bus

Liao, J., Cai, J., Zhou, Y., Ren, J., & Huang, T. (2026) · International Journal of Electrical Power & Energy Systems · doi:10.1016/j.ijepes.2026.111959

The fantasy of digital immateriality: AI, ecologically unequal exchange, and appropriation of lived human time

Pieper, M., & Hornborg, A. (2026) · Ecological Economics · doi:10.1016/j.ecolecon.2026.109251

Can coal-resource cities achieve high-quality coordination of industry, environment, and carbon? Evidence from Huainan, China

Wu, H., Su, C., Zhang, Y., Li, M., Kong, X., Liu, Y., & Gao, L. (2026) · Environmental and Sustainability Indicators · doi:10.1016/j.indic.2026.101541

Bear-ing the Burden: Impact of Affective Eco-Feedback on Sustainable LLM Use

Okonji, J. A., Wu, S.-J., & Michahelles, F. (2026) · Nordic Conference on Human-Computer Interaction · doi:10.1145/3829807.3829859

Artificial Intelligence and Sustainable Energy Transition: Innovation–Utilization Pathways and the Contingent Roles of Political, Financial, and Economic Stability

Li, R., Li, Y., & Wang, Q. (2026) · Sustainable Development · doi:10.1002/sd.71691

Energy infrastructure construction and enterprises’ Artificial Intelligence development: Evidence from China’s ultra-high voltage transmission projects

Lin, W., Guo, Y., Wang, B., & Cheng, Q. (2026) · PLoS ONE · doi:10.1371/journal.pone.0355269

Harnessing Artificial Intelligence and Technological Innovations for SDG 9

OBINNA, A. S., Onah, F. N., & Alabi, J. A. (2026) · Emerald Group Publishing Limited eBooks · doi:10.1108/978-1-80592-200-120261009

The growth of generative artificial intelligence as a factor of investment transformation of the energy system based on the development of an integral index of energy intensity of data centers

KHARABADZE, N. V., Gromova, A. A., GEREEV, A. M., PODPLUTOV, D. R., & PASTERNAK, G. S. (2026) · Economic Analysis Theory and Practice · doi:10.24891/lacqqf

Intergenerational Green Artificial Intelligence Governance: Operationalising Environmental Accountability in Sustainable Artificial Intelligence Systems

Parul, S., Magoge, J. S., Chatterjee, I. B., Mbilinyi, P. J., & Nandivelugu, M. (2026) · Applied AI Letters · doi:10.1002/ail2.70038

Governed Syntax-Informed AI Adaptation for Sustainable Academic Reading: Fidelity, Comprehension, and Near-Transfer

Alrashed, M. A. (2026) · Sustainability · doi:10.3390/su181910171

Moving beyond thermal conductivity to interfacial networks for next-generation coolants

Bons, J., Kabir, M. H., Routh, A., & Liang, H. Y. (2026) · Cell Reports Physical Science · doi:10.1016/j.xcrp.2026.103591

AI-oriented policy and urban ecological quality: Evidence from China's national AI innovation and development pilot zones

Wang, S., Zhou, L., Ling, Z., & Wan, S. (2026) · Cities · doi:10.1016/j.cities.2026.107640

Synergistic Enhancement of HZO Ferroelectric Properties via La2O3 Interface Engineering for Neuromorphic Computing

Wang, X.-X., Ma, Y.-J., Wei, J.-Y., Zhang, C., Zhang, X., Zheng, H., Li, J.-H., Yuan, H.-X., Wang, J., Cao, Y., et al. (2026) · ACS Applied Materials & Interfaces · doi:10.1021/acsami.6c12825

Gula: consumir recursos y producir contenido sin medida

Navia, J. M. A., Zapata, E. M. J., & Castillo, V. S. (2026) · doi:10.62486/978-9915-9928-6-0.ch05

Informing users about LLMs' environmental costs encourages ethical consumption

Rabinovitch, H., Kashi, I., & Shalvi, S. (2026) · Computers in Human Behavior · doi:10.1016/j.chb.2026.109214

Not Just How Much, But When: Projecting AI-Driven Data Center Electricity Demand and Hourly Carbon Emissions in KoreaPreprint

Kim, J., Lee, J., Kim, K., & Kim, J. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.7592549

Digital ambition under ecological constraints: Governing AI in climate-vulnerable South Africa

Hlabisa, S. B. (2026) · Social Sciences & Humanities Open · doi:10.1016/j.ssaho.2026.103800

Artificial Intelligence Policy and Urban Carbon Emission Performance: Evidence from the Yangtze River Delta, China

Guo, Z., & Lu, H. (2026) · Ecological Chemistry and Engineering S · doi:10.2478/eces-2026-0020

From AI Compute Growth to Grid Stress: A Conditional Scenario-Screening Framework for the EU-27 and the United Kingdom to 2035

Szeberényi, A. (2026) · Energies · doi:10.3390/en19204817

Artificial intelligence-driven low-carbon transition in energy consumption structure

Zhang, H., Zhang, C., Ning, Y., & Xu, Z. (2026) · Chinese Journal of Population Resources and Environment · doi:10.1016/j.cjpre.2026.08.005

The Algorithmic Energy Crisis

Shah, M., Rahu, M. A., Raza, M. A., Tunio, N. A., & Chandio, A. F. (2026) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-7549-6.ch004

From Queries to Tasks: Rethinking How We Measure AI’s Environmental Footprint

Kshetri, N., & Kshetri, N. (2026) · IT Professional · doi:10.1109/mitp.2026.3732774

The Energy Economics of Artificial Intelligence Data Centers: Demand Uncertainty, System Costs, Flexibility, and Credible Decarbonization

Magazzino, C., & Fakher, H. A. (2026) · Energies · doi:10.3390/en19204890 · Cited by 1

Does Lower Precision Always Save Energy? Benchmarking Energy–Performance Trade-Offs in Weight-Quantized Large Language Model Inference on a GPU

Laskowski, J. F., Tomiło, P., Kozłowski, E., Laskowska, A., & Piątek, R. K. (2026) · Energies · doi:10.3390/en19204893

Generative Artificial Intelligence in Marketing: Consumer Responses, Authenticity, and Governance Challenges

Suat, O. K. A. Y. (2026) · Özgür Yayınları eBooks · doi:10.58830/ozgur.pub1533.c6201

From Boltzmann to Quantum Civilization: Thermodynamic Order and Irreversibility in Semiconductor-Enabled FuturesPreprint

Hsiao, C. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.7649721

SMR–renewable microgrids for 24/7 carbon-free energy matching in data centers: Techno-economic performance and sustainability indicators

Yoon, S., Cho, K.-W., & Park, S. (2026) · Sustainable Energy Technologies and Assessments · doi:10.1016/j.seta.2026.105483

Two-Stage Energy Scheduling for Multi-Tenant Data Centers: Day-Ahead Workload Coordination and Real-Time Rolling Dispatch

Wu, W., Yang, X., Wang, Y., & Yang, Y. (2026) · Processes · doi:10.3390/pr14213387

Future Trends in AI-Driven Sustainability Education and Research

Uden, L., Sulaiman, F., & Ching, G. (2026) · World sustainability series · doi:10.1007/978-3-032-34574-5_24

Toward sustainable on-device intelligence: A survey on energy-efficient RAG systems with small language models

Cheng, Z., Lai, L., Liu, Y., & Sun, Y. (2026) · Neurocomputing · doi:10.1016/j.neucom.2026.135448 · Cited by 1

The Necessity

Inderwildi, O. R. (2026) · SpringerBriefs in applied sciences and technology · doi:10.1007/978-3-032-37307-6_1

Thermal integration of combined cycle power plants with methylcyclohexane dehydrogenation: the hydrogen versus power trade-off for heat extraction from flue gas and steamPreprint

Lim, H. S., Lee, I., Kim, J. Y., Kim, K., Sohn, H., Jeong, H., Kim, Y., Nam, S. W., Won, W., & Bae, M. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.7661411

Renewable energy-driven green hydrogen production: A comprehensive review of technologies, techno-economics, life cycle assessment and digitalization pathways

Kumar, J., Baroniya, R., Verma, T. N., & Mandal, S. (2026) · Energy · doi:10.1016/j.energy.2026.142848

Distributed Systems in the Age of AI: A Systematic Review of Sustainability Practices, Metrics, and Frameworks

Ghanim, N. Y. (2026) · Al-Noor Journal of Engineering Management and Computer Science · doi:10.71229/pt97m783

Digitalization and artificial intelligence in hydrogen storage: From materials discovery to smart system operation

Kumar, J., Baroniya, R., Kumar, R., & Bowade, A. (2026) · International Journal of Hydrogen Energy · doi:10.1016/j.ijhydene.2026.158171

Cool down and amp up: radiative cooling boosts power grid cable ampacity and safety

Li, Q., & Hsu, P. (2026) · eLight · doi:10.1186/s43593-026-00147-8

How to Scream into the Wind Without Losing Your Voice: A Behavioral Analysis of LLM Use and Environmental Impact

Xie, S., Meshes, E., Jennings, A. M., & Messer, A. (2026) · Behavior Analysis in Practice · doi:10.1007/s40617-026-01239-0

Sinkhorn Distributionally Robust Co-Optimization of Data Center Electricity-Computing-Thermal Flexibility With Conditional Regional ProbabilitiesPreprint

翁泽滨, Liu, C., Li, Z., Jin, Y., Hu, R., Wang, Z., Bai, D., Zhang, X., Luo, Y., & Han, J. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.7674329

Can New Energy Futures Serve as Effective Hedging Instruments for AI Industry Chain Assets? Evidence from ChinaPreprint

liu, L., Su, X., & Zhu, J. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.7674954

Sustainable Energy for Industry 5.0: AI-Enabled Energy Management, AI Infrastructure, and the Energy Quadrilemma

Hrabovska, N., Budinská, I., Zolotová, I., Kajáti, E., & Kenyeres, M. (2026) · Sustainability · doi:10.3390/su182110928

Greening GenAI: sustainable solutions for higher education

de Koning, B. B., & Nederhand, M. L. (2026) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2026.1961109

The Data Center Economy

Nosios, M., Bitzenis, A., & Κουτσουπιάς, Ν. (2026) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-9469-5.ch008

Energy and Carbon Footprint of Artificial Intelligence: Measurement, Disagreement and Mitigation

Rasmi, M. (2026) · International Journal of Information Technology Research Studies (IJITRS) · doi:10.63090/ijitrs/3139.3209.0038

Computing-power collaborative energy management for underwater microgrids via safe deep reinforcement learning

Fang, Z., Zhou, H., Sun, M., Guo, F., & Li, P. (2026) · Electric Power Systems Research · doi:10.1016/j.epsr.2026.114420

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