IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v17y2025i7p2887-d1619435.html
   My bibliography  Save this article

The Energy Hunger Paradox of Artificial Intelligence: End of Clean Energy or Magic Wand for Sustainability?

Author

Listed:
  • Hafize Nurgul Durmus Senyapar

    (Faculty of Health Sciences, Gazi University, Ankara 06490, Türkiye)

  • Ramazan Bayindir

    (Faculty of Technology, Gazi University, Ankara 06560, Türkiye)

Abstract

Artificial Intelligence (AI) plays a dual role in the clean energy transition, acting both as a major energy consumer and as a driver of sustainability. While AI enhances renewable energy forecasting, optimizes smart grids, and improves energy storage efficiency, the rapid growth of AI-driven data centers has significantly increased global electricity demand. AI-related energy consumption is projected to double by 2026 and triple by 2030, accounting for approximately 1.3% of global electricity use. This study adopts a multidisciplinary approach, synthesizing engineering, business, and policy insights to evaluate AI’s energy footprint and contributions to sustainability. The findings reveal that AI-driven optimization enhances smart grid efficiency and forecasting accuracy; however, infrastructure limitations, regulatory gaps, and economic constraints hinder AI’s alignment with sustainability goals. The results are systematically structured across five key themes: key findings, impact on energy consumption, risks and challenges, potential solutions, and policies and regulations. Supported by thematic tables and an original infographic, this study provides a comprehensive analysis of AI’s evolving role. By integrating AI with global sustainability policies, stakeholders can leverage its potential to accelerate the clean energy transition while minimizing the ecological footprint.

Suggested Citation

  • Hafize Nurgul Durmus Senyapar & Ramazan Bayindir, 2025. "The Energy Hunger Paradox of Artificial Intelligence: End of Clean Energy or Magic Wand for Sustainability?," Sustainability, MDPI, vol. 17(7), pages 1-32, March.
    • Handle: RePEc:gam:jsusta:v:17:y:2025:i:7:p:2887-:d:1619435
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/17/7/2887/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/17/7/2887/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Jin, Chaoqiang & Bai, Xuelian & Yang, Chao & Mao, Wangxin & Xu, Xin, 2020. "A review of power consumption models of servers in data centers," Applied Energy, Elsevier, vol. 265(C).
    2. Rocha, Helder R.O. & Honorato, Icaro H. & Fiorotti, Rodrigo & Celeste, Wanderley C. & Silvestre, Leonardo J. & Silva, Jair A.L., 2021. "An Artificial Intelligence based scheduling algorithm for demand-side energy management in Smart Homes," Applied Energy, Elsevier, vol. 282(PA).
    3. Raghu Raman & Sangeetha Gunasekar & Deepa Kaliyaperumal & Prema Nedungadi, 2024. "Navigating the Nexus of Artificial Intelligence and Renewable Energy for the Advancement of Sustainable Development Goals," Sustainability, MDPI, vol. 16(21), pages 1-25, October.
    4. Dong, Zequn & Tan, Chaodan & Ma, Biao & Ning, Zhaoshuo, 2024. "The impact of artificial intelligence on the energy transition: The role of regulatory quality as a guardrail, not a wall," Energy Economics, Elsevier, vol. 140(C).
    5. Salil Bharany & Sandeep Sharma & Osamah Ibrahim Khalaf & Ghaida Muttashar Abdulsahib & Abeer S. Al Humaimeedy & Theyazn H. H. Aldhyani & Mashael Maashi & Hasan Alkahtani, 2022. "A Systematic Survey on Energy-Efficient Techniques in Sustainable Cloud Computing," Sustainability, MDPI, vol. 14(10), pages 1-89, May.
    6. Dalia Mohammed Talat Ebrahim Ali & Violeta Motuzienė & Rasa Džiugaitė-Tumėnienė, 2024. "AI-Driven Innovations in Building Energy Management Systems: A Review of Potential Applications and Energy Savings," Energies, MDPI, vol. 17(17), pages 1-35, August.
    7. Antonopoulos, Ioannis & Robu, Valentin & Couraud, Benoit & Kirli, Desen & Norbu, Sonam & Kiprakis, Aristides & Flynn, David & Elizondo-Gonzalez, Sergio & Wattam, Steve, 2020. "Artificial intelligence and machine learning approaches to energy demand-side response: A systematic review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 130(C).
    8. Boza, Pal & Evgeniou, Theodoros, 2021. "Artificial intelligence to support the integration of variable renewable energy sources to the power system," Applied Energy, Elsevier, vol. 290(C).
    9. Sabina-Cristiana Necula, 2023. "Assessing the Potential of Artificial Intelligence in Advancing Clean Energy Technologies in Europe: A Systematic Review," Energies, MDPI, vol. 16(22), pages 1-24, November.
    10. Groppi, Daniele & Pfeifer, Antun & Garcia, Davide Astiaso & Krajačić, Goran & Duić, Neven, 2021. "A review on energy storage and demand side management solutions in smart energy islands," Renewable and Sustainable Energy Reviews, Elsevier, vol. 135(C).
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Vladimir Franki & Darin Majnarić & Alfredo Višković, 2023. "A Comprehensive Review of Artificial Intelligence (AI) Companies in the Power Sector," Energies, MDPI, vol. 16(3), pages 1-35, January.
    2. Boza, Pal & Evgeniou, Theodoros, 2021. "Artificial intelligence to support the integration of variable renewable energy sources to the power system," Applied Energy, Elsevier, vol. 290(C).
    3. Kanakadhurga, Dharmaraj & Prabaharan, Natarajan, 2022. "Demand side management in microgrid: A critical review of key issues and recent trends," Renewable and Sustainable Energy Reviews, Elsevier, vol. 156(C).
    4. Prina, Matteo Giacomo & Groppi, Daniele & Nastasi, Benedetto & Garcia, Davide Astiaso, 2021. "Bottom-up energy system models applied to sustainable islands," Renewable and Sustainable Energy Reviews, Elsevier, vol. 152(C).
    5. Hasan M. Salman & Jagadeesh Pasupuleti & Ahmad H. Sabry, 2023. "Review on Causes of Power Outages and Their Occurrence: Mitigation Strategies," Sustainability, MDPI, vol. 15(20), pages 1-34, October.
    6. Uz Zaman, Qamar & Zhao, Yuhuan & Zaman, Shah & Batool, Kiran & Nasir, Rabiya, 2024. "Reviewing energy efficiency and environmental consciousness in the minerals industry Amidst digital transition: A comprehensive review," Resources Policy, Elsevier, vol. 91(C).
    7. Caixin Yan & Zhifeng Qiu, 2025. "Review of Power Market Optimization Strategies Based on Industrial Load Flexibility," Energies, MDPI, vol. 18(7), pages 1-41, March.
    8. Østergaard, P.A. & Lund, H. & Thellufsen, J.Z. & Sorknæs, P. & Mathiesen, B.V., 2022. "Review and validation of EnergyPLAN," Renewable and Sustainable Energy Reviews, Elsevier, vol. 168(C).
    9. Gao, Datong & Zhao, Bin & Kwan, Trevor Hocksun & Hao, Yong & Pei, Gang, 2022. "The spatial and temporal mismatch phenomenon in solar space heating applications: status and solutions," Applied Energy, Elsevier, vol. 321(C).
    10. Xu, Jing & Wang, Xiaoying & Gu, Yujiong & Ma, Suxia, 2023. "A data-based day-ahead scheduling optimization approach for regional integrated energy systems with varying operating conditions," Energy, Elsevier, vol. 283(C).
    11. Zhao, Qian & Wang, Lu & Stan, Sebastian-Emanuel & Mirza, Nawazish, 2024. "Can artificial intelligence help accelerate the transition to renewable energy?," Energy Economics, Elsevier, vol. 134(C).
    12. Oleksandr Miroshnyk & Oleksandr Moroz & Taras Shchur & Andrii Chepizhnyi & Mohamed Qawaqzeh & Sławomir Kocira, 2023. "Investigation of Smart Grid Operation Modes with Electrical Energy Storage System," Energies, MDPI, vol. 16(6), pages 1-13, March.
    13. Fuster-Palop, Enrique & Prades-Gil, Carlos & Masip, X. & Viana-Fons, Joan D. & Payá, Jorge, 2021. "Innovative regression-based methodology to assess the techno-economic performance of photovoltaic installations in urban areas," Renewable and Sustainable Energy Reviews, Elsevier, vol. 149(C).
    14. Wu, Chunying & Sun, Lingfang & Piao, Heng & Yao, Lijia, 2024. "Adaptive fuzzy finite time integral sliding mode control of the coordinated system for 350 MW supercritical once-through boiler unit to enhance flexibility," Energy, Elsevier, vol. 302(C).
    15. Akashdeep Bhardwaj & Keshav Kaushik & Mashael S. Maashi & Mohammed Aljebreen & Salil Bharany, 2022. "Alternate Data Stream Attack Framework to Perform Stealth Attacks on Active Directory Hosts," Sustainability, MDPI, vol. 14(19), pages 1-19, September.
    16. Davide Coraci & Silvio Brandi & Marco Savino Piscitelli & Alfonso Capozzoli, 2021. "Online Implementation of a Soft Actor-Critic Agent to Enhance Indoor Temperature Control and Energy Efficiency in Buildings," Energies, MDPI, vol. 14(4), pages 1-26, February.
    17. Omar Al-Ani & Sanjoy Das, 2022. "Reinforcement Learning: Theory and Applications in HEMS," Energies, MDPI, vol. 15(17), pages 1-37, September.
    18. Pacheco, A. & Monteiro, J. & Santos, J. & Sequeira, C. & Nunes, J., 2022. "Energy transition process and community engagement on geographic islands: The case of Culatra Island (Ria Formosa, Portugal)," Renewable Energy, Elsevier, vol. 184(C), pages 700-711.
    19. Psarros, Georgios N. & Papathanassiou, Stavros A., 2023. "Generation scheduling in island systems with variable renewable energy sources: A literature review," Renewable Energy, Elsevier, vol. 205(C), pages 1105-1124.
    20. Ahmad, Tanveer & Madonski, Rafal & Zhang, Dongdong & Huang, Chao & Mujeeb, Asad, 2022. "Data-driven probabilistic machine learning in sustainable smart energy/smart energy systems: Key developments, challenges, and future research opportunities in the context of smart grid paradigm," Renewable and Sustainable Energy Reviews, Elsevier, vol. 160(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:17:y:2025:i:7:p:2887-:d:1619435. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.