Sustainable AI: Emerging Trends, Impacts, and Future Challenges
Abstract
Sustainable AI considers both the environmental impact of AI technologies and their role in advancing global sustainability. This study provides a comprehensive analysis of emerging trends in sustainable AI, highlighting energy-efficient algorithms, green data centers, AI-driven resource management, and ethical governance. It emphasizes AI's dual nature, its potential to drive climate action and socio-economic progress, alongside risks such as carbon emissions, e-waste, and digital inequality. The study underscores the need for interdisciplinary collaboration, inclusive policies, and standardized sustainability metrics to ensure responsible AI deployment. By identifying barriers and proposing strategies, it offers guidance to researchers, policymakers, and industry leaders on aligning AI with long-term environmental, ethical, and social objectives.
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Cyberbullying classifiers and natural language processing algorithms are primarily developed to analyze and detect cyberbullying instances in English text.
“Firstly, cyberbullying classifiers and natural language processing algorithms are primarily developed to analyze and detect cyberbullying instances in English text [125,126].”
From Cyberbullying detection: Exploring datasets, technologies, and approaches on social media platforms (2026) · cites this paper as [125]
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Gahlot, N., & Nautiyal, O. P. (2024) · Indian Journal of Forestry · doi:10.54207/bsmps1000-2024-kb5b23 · Cited by 5 · see citing works
Rao, B., Lipizzi, C., & Mansouri, M. (2024) · IEEE International Symposium on Systems Engineering · doi:10.1109/isse63315.2024.10741091 · Cited by 5 · see citing works
Ashatkar, A. (2024) · International Journal for Research in Applied Science and Engineering Technology · doi:10.22214/ijraset.2024.58806 · Cited by 4 · see citing works
Energy Efficiency in AI for 5G and Beyond: A DeepRx Case Study
Lbath, A., & Labriji, I. (2024) · Joint European Conference on Networks and Communications & 6G Summit (EuCNC/6G Summit) · doi:10.1109/eucnc/6gsummit60053.2024.10597065 · Cited by 4 · see citing works
AI Models for Edge Computing: Hardware-aware Optimizations for Efficiency
Li, H. H. (2024) · doi:10.23919/date58400.2024.10546543 · Cited by 4 · see citing works
A Comprehensive Sustainable Framework for Machine Learning and Artificial Intelligence
Pagliari, R., Hill, P., Chen, P.-Y., Dabrowny, M., Tan, T., & Buet-Golfouse, F. (2024) · Frontiers in artificial intelligence and applications · doi:10.3233/faia240569 · Cited by 4 · see citing works
Driving Business Sustainability Through Effective Marketing Leveraging on Artificial Intelligence
Mtengwa, E., Muchenje, C., Maregere, L. L., & Masengu, R. (2024) · Advances in marketing, customer relationship management, and e-services book series · doi:10.4018/979-8-3693-6760-5.ch005 · Cited by 4 · see citing works
Discriminatory data yields discriminatory systems: when AI biases harm human beings
Entschew, E. M. (2024) · Edward Elgar Publishing eBooks · doi:10.4337/9781803928241.00009 · Cited by 4 · see citing works
(2021) · Encyclopedia of the UN sustainable development goals · doi:10.1007/978-3-319-95873-6_300182 · Cited by 3 · see citing works
ENVIRONMENTAL ASPECTS OF E-WASTE MANAGEMENT
Sahoo, P. K. (2023) · Iterative International Publishers eBooks · doi:10.58532/v3bdcs1ch21 · Cited by 3 · see citing works
Susmitha, K., Srimlika, N., Sharma, V., Almusawi, M., & Mishra, S. (2023) · International Conference for Technological Engineering and its Applications in Sustainable Development (ICTEASD) · doi:10.1109/icteasd57136.2023.10584909 · Cited by 3 · see citing works
AI-Driven Risk Management for Sustainable Enterprise Development: A Review of Key Risks
Guo, Z., & Li, R. (2024) · International Journal of Business and Management · doi:10.5539/ijbm.v19n6p82 · Cited by 3 · see citing works
Optimizing Deep Learning Efficiency through Algorithm-Hardware Co-design
Santoso, J. T., Wibowo, M. C., & Raharjo, B. (2024) · Journal of Advances in Information Technology · doi:10.12720/jait.15.10.1163-1173 · Cited by 3 · see citing works
Healing AI Mental Health to Slash Energy Consumption and Carbon Emissions
Misan, S., Orciuolo, P., Beebeejaun, R., Bosich, D., Chiandone, M., & Sulligoi, G. (2024) · AEIT International Annual Conference · doi:10.23919/aeit63317.2024.10736831 · Cited by 3 · see citing works
The Intersection of Technology and Social Sustainability
Rajendran, R. K., Priya, T. M., Immanuel, A., V, R, W. B. N., Chitrarasu, K., Vetrivel, S., Paulraj, A. K. J., & Kokila, S. S. (2024) · Practice, progress, and proficiency in sustainability · doi:10.4018/979-8-3693-6298-3.ch017 · Cited by 3 · see citing works
Trends in Hardware-Based AL and ML
Gupta, P., & Sehgal, N. K. (2021) · Springer eBooks · doi:10.1007/978-3-030-71270-9_9 · Cited by 2 · see citing works
Challenges to Implement Artificial Intelligence for Environmental Sustainability
Mogha, H., & Hasteer, N. (2023) · Lecture notes in civil engineering · doi:10.1007/978-981-99-1388-6_31 · Cited by 2 · see citing works
AI Hardware Resource Monitoring in the Data Center Environment
Solution Architect, D. T. B. #. 4., & Saradhi, N. V. (2024) · INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT · doi:10.55041/ijsrem36782 · Cited by 2 · see citing works
The Impact of AI on Sustainable Development of The Labor Market
Ni, Y., Qi, M., Xie, C., & Wu, Y. (2024) · SHS Web of Conferences · doi:10.1051/shsconf/202420002016 · Cited by 2 · see citing works
Data Center Location Planning for Social Network Data
Oshan, T., Hamada, N. H., & Elamsy, T. A. (2023) · International Conference on Social Networks Analysis, Management and Security (SNAMS) · doi:10.1109/snams60348.2023.10375465 · Cited by 1 · see citing works
TACKLING OF ENVIRONMENT BY ARTIFICIAL INTELLIGENCE
Verma, P., & Kalra, D. A. (2024) · Iterative International Publishers eBooks · doi:10.58532/nbennurch303 · Cited by 1 · see citing works
Digital Learning and Social Sustainability
Yadav, N. S., Yadav, P. S., & Goar, V. K. (2024) · Advances in educational technologies and instructional design book series · doi:10.4018/979-8-3693-1854-6.ch001 · Cited by 1 · see citing works
Vemulapalli, G. (2024) · CRC Press eBooks · doi:10.1201/9781003497189-16 · Cited by 1 · see citing works
Transparency and Secrecy as a Spectrum in AI-assisted Trustworthy Public Decision-making
Varošanec, I. (2024) · doi:10.33612/diss.1161808642 · Cited by 1 · see citing works
Nagar, M. R. (2024) · Practice, progress, and proficiency in sustainability · doi:10.4018/979-8-3693-3410-2.ch008 · Cited by 1 · see citing works
As AI giants duel, the Global South builds its own brainpower
Biyela, A. R. A. S. R. S. (2025) · Nature Middle East · doi:10.1038/nmiddleeast.2025.65 · Cited by 1 · see citing works
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Cyberbullying detection: Exploring datasets, technologies, and approaches on social media platforms
Philipo, A. G., Sarwatt, D. S., Ding, J., Daneshmand, M., & Ning, H. (2026) · ACM Computing Surveys · doi:10.1145/3785654 · Cited by 14 · see citing works
Self-citationMarmouzi, O., Oumaira, I., & Khaddar, M. A. E. (2026) · Sustainability · doi:10.3390/su18084115 · Cited by 7 · see citing works
Can renewable energy meet the surging power demand of artificial intelligence? A systematic review
Yoo, S., Choi, S., & Kim, D. (2026) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2026.117213 · Cited by 4 · see citing works
Ayana, J. W., Lifelo, Z., Ning, H., & Ding, J. (2026) · IEEE Internet of Things Journal · doi:10.1109/jiot.2026.3702095 · Cited by 1 · see citing works
Self-citationThe Price of Intelligence: Can AI Afford to Be Sustainable?
Mastropaolo, A. (2026) · Computer · doi:10.1109/mc.2026.3670645
Shati, I. A. K., Gernaz, M. F. S., Jernz, R. S. S., & Arfa, A. S. A. (2026) · International Conference on Computing, Networks and Internet of Things (CNIOT) · doi:10.1109/cniot69547.2026.11582687
AI-Driven Sustainable Marketing for Sustainable Tourism
Balushi, M. K. A., Kalbani, F. A., & Soliman, M. S. A. (2026) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-8475-7.ch002
Philipo, A. G., Ding, J., Sarwatt, D. S., & Ning, H. (2026) · Information Fusion · doi:10.1016/j.inffus.2026.104820
Self-citationTSA-Net+: a communication-aware hybrid ANN-SNN framework for energy-efficient IoT inference
Meshram, N., Anand, V., & Tiwari, V. (2026) · Internet of Things · doi:10.1016/j.iot.2026.102117
Dautova, S. B., Abdumannopovna, R. M., Kazimbetova, Z. Z., Баходировна, М. Н., Bayniyazova, G. M., Utebaev, T. T., & Kuchiboyev, S. A. (2026) · Eurasia Journal of Mathematics Science and Technology Education · doi:10.29333/ejmste/19560
PAULA, C. D. F. D., & de Oliveira, M. G. (2026) · Anais ... Encontro Nacional de Engenharia de Produção/Anais do Encontro Nacional de Engenharia de Produção · doi:10.14488/enegep2026_tn_wpg_444_2204_53086
- Authors
- Philipo, A. G., Ning, H., Sarwatt, D. S., Mohamed, J. A., Yusufu, A. S., Shi, F., Urenje, S., & Ding, J.
- Affiliations
- University of Science and Technology Beijing (CN)
- Dar es Salaam Institute of Technology (TZ)
- Uppsala University (SE)
- Blekinge Institute of Technology (SE)
- ORCID
- Venue
- IEEE Transactions on Sustainable Computing
- Published
- 2025-11
- Volume
- 10
- Issue
- 6
- Pages
- 1278-1291
- ISSN
- 2377-3782 (ISSN-L)
- 2377-3790
- OpenAlex
- W4414270498
- Cited by
- 9
- References
- 127
- Citations by year
- 2026: 8 · 2025: 1
- FWCI
- 2.03
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- IEEE Transactions on Sustainable Computing (publishedVersion)
- Topics
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Green IT and Sustainability
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Social Sciences › Sociology and Political Science › Innovation, Sustainability, Human-Machine Systems
- Keywords
- sustainable AI, environmental impact of AI, energy-efficient algorithms, green data centers, ethical AI governance, climate action, CO2 emissions, electronic waste, digital inequality, sustainability indicators, responsible AI deployment
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
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