Skip to main content
SpringerLink
Log in

Greening Telecom: Harnessing the Power of Artificial Intelligence for Sustainable Communications

  • Conference paper
  • First Online:
Biologically Inspired Cognitive Architectures 2023 (BICA 2023)

Part of the book series: Studies in Computational Intelligence ((SCI,volume 1130))

Included in the following conference series:

  • 172 Accesses

Abstract

The article explores the potential for artificial intelligence (AI) technologies to reduce the environmental impact of the information and communications technology (ICT) industry. The paper notes that the ICT sector is responsible for a significant proportion of global carbon emissions, but that digital solutions can help reduce emissions and promote sustainable practices. The study finds that the use of AI in telecommunications has great potential to reduce the industry's environmental impact by optimizing energy efficiency and reducing carbon emissions. Despite challenges such as data quality, limited availability of data, and ethical and privacy concerns, continued research and development is crucial in realizing the full potential of AI-powered green technologies. The article concludes that by harnessing the power of AI, the telecommunications industry can play a significant role in mitigating the effects of climate change and creating a more sustainable future.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 219.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 279.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Intergovernmental Panel on Climate Change: Synthesis Report of the IPCC Sixth Assessment Report (AR6): Summary for Policymakers. Intergovernmental Panel on Climate Change (2023)

    Google Scholar 

  2. Intergovernmental Panel on Climate Change: Mitigation of Climate Change. Working Group III Contribution to the IPCC Sixth Assessment Report (2022)

    Google Scholar 

  3. Yousaf, L., Ge, S., Zeeshan, F., Salman, A., Muhammad, A.B.: Do financial development and energy efficiency ensure green environment? Evidence from R.C.E.P. economies. Econ. Res. Ekon. Istraž. 36(1), 51–72 (2023). https://doi.org/10.1080/1331677X.2022.2066555

  4. Paris Agreement to the United Nations Framework Convention on Climate Change. T.I.A.S. No. 16-1104, 12 Dec 2015

    Google Scholar 

  5. Fetting, C.: The European Green Deal ESDN Report. ESDN Office, Vienna (2020)

    Google Scholar 

  6. Zhang, X., Wang, Y.: How to reduce household carbon emissions: a review of experience and policy design considerations. Energy Policy 102, 116–124 (2017). https://doi.org/10.1016/j.enpol.2016.12.010

    Article  Google Scholar 

  7. Imam, N., Hossain, M., Saha, T.: Potentials and Challenges of Using ICT for Climate Change Adaptation: A Study of Vulnerable Community in Riverine Islands of Bangladesh (2017). https://doi.org/10.1007/978-3-319-56523-1_7

  8. Freitag, C., Berners-Lee, M., Widdicks, K., Knowles, B., Blair, G.S., Friday, A.: The real climate and transformative impact of ICT: A critique of estimates, trends, and regulations. Patterns 2(9), 100340 (2021). https://doi.org/10.1016/j.patter.2021.100340

    Article  Google Scholar 

  9. International Telecommunication Union: Recommendation ITU-T L.1470 Greenhouse Gas Emissions Trajectories for the Information and Communication Technology Sector Compatible with the UNFCCC Paris Agreement (2020)

    Google Scholar 

  10. ITU, GESI, GSMA, SBTI: Guidance for ICT Companies Setting Science Based Targets (2020)

    Google Scholar 

  11. Khan, M.: AI-enabled transformations in telecommunications industry. Telecommun. Syst.. Syst. 82, 1–2 (2023). https://doi.org/10.1007/s11235-022-00989-w

    Article  Google Scholar 

  12. Morley, J., Widdicks, K., Hazas, M.: Digitalisation, energy and data demand: The impact of Internet traffic on overall and peak electricity consumption. Energy Res. Soc. Sci. 38, 128–137 (2018). https://doi.org/10.1016/j.erss.2018.01.018

    Article  Google Scholar 

  13. Huawei Homepage: https://www.huawei.com/en/news/2022/10/intelligent-5g-mbbf-2022. Last accessed 15 June 2023

  14. Telefónica Homepage: https://www.telefonica.com/en/communication-room/telefonica-drives-energy-consumption-optimisation-through-solutions-based-on-artificial-intelligence-and-machine-learning/. Last accessed 15 June 2023

  15. Rojek, I., JasiulewiczKaczmarek, M., Piechowski, M., Mikołajewski, D.: An artificial intelligence approach for improving maintenance to supervise machine failures and support their repair. Appl. Sci. 13, 4971 (2023). https://doi.org/10.3390/app13084971

    Article  Google Scholar 

  16. United Nations University Homepage: https://unu.edu/press-release/global-e-waste-surging-21-5-years. Last accessed 15 June 2023

  17. Mobile World Live Homepage: https://www.mobileworldlive.com/huawei-updates/calvin-zhao-of-huawei-intelligentran-bringing-intelligent-network-into-reality/. Last accessed 15 June 2023

  18. Balasooriya, P., Wibowo, S., Wells, M.: Green cloud computing and economics of the cloud. J. Comput. 5 (2016)

    Google Scholar 

  19. Andrae, A.S.G., Edler, T.: On global electricity usage of communication technology: trends to 2030. Challenges 6(1), 117–157 (2015). https://doi.org/10.3390/challe6010117

    Article  Google Scholar 

  20. IEA: Data Centres and Data Transmission Networks, IEA, Paris. https://www.iea.org/reports/data-centres-and-data-transmission-networks, License: CC BY 4.0 (2022)

  21. Han, Z., Sun, X., Wei, H., Ji, Q., Xue, D.: Energy saving analysis of evaporative cooling composite air conditioning system for data centers. Appl. Therm. Eng. 186, 116506 (2021). https://doi.org/10.1016/j.applthermaleng.2020.116506

    Article  Google Scholar 

  22. Park, B.R., Choi, Y.J., Choi, E.J., Moon, J.W.: Adaptive control algorithm with a retraining technique to predict the optimal amount of chilled water in a data center cooling system. J. Build. Eng. 50, 104167 (2022). https://doi.org/10.1016/j.jobe.2022.104167

    Article  Google Scholar 

  23. Conti, G., Jimenez, D., del Rio, A., Castano-Solis, S., Serrano, J., Fraile-Ardanuy, J.: A multi-port hardware energy meter system for data centers and server farms monitoring. Sensors 23(1), 119 (2023). https://doi.org/10.3390/s23010119

    Article  Google Scholar 

  24. Tanveer, A., et al.: Artificial intelligence in sustainable energy industry: Status Quo, challenges and opportunities. J. Clean. Prod. 289, 125834 (2021). https://doi.org/10.1016/j.jclepro.2021.125834

    Article  Google Scholar 

  25. Sutton-Parker, J.: Is sufficient carbon footprint information available to make sustainability focused computer procurement strategies meaningful? Proc. Comput. Sci. 203, 280–289 (2022). https://doi.org/10.1016/j.procs.2022.07.036

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. IXP Consulting, Kirovogradskaya 32-2-84, 117519, Moscow, Russian Federation

    Anastasiia Suslina & Konstantin Savin

  2. National Research Nuclear University MEPhI (Moscow Engineering Physics Institute), 31 Kashirskoye Shosse St., 115409, Moscow, Russian Federation

    Irina Suslina

Authors
  1. Anastasiia Suslina
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Konstantin Savin
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Irina Suslina
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Anastasiia Suslina .

Editor information

Editors and Affiliations

  1. Cybernetics Department, Moscow Engineering Physics Institute, Moscow, Russia

    Alexei V. Samsonovich

  2. College of Science and Technology, Ningbo University, Ningbo, China

    Tingting Liu

Rights and permissions

Reprints and permissions

Copyright information

© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Suslina, A., Savin, K., Suslina, I. (2024). Greening Telecom: Harnessing the Power of Artificial Intelligence for Sustainable Communications. In: Samsonovich, A.V., Liu, T. (eds) Biologically Inspired Cognitive Architectures 2023. BICA 2023. Studies in Computational Intelligence, vol 1130. Springer, Cham. https://doi.org/10.1007/978-3-031-50381-8_94

Download citation

Publish with us

Policies and ethics

Search

Navigation