IDEAS home Printed from https://ideas.repec.org/a/pop/procee/v13y2025p371-381.html

Green AI power impact on development of smart cities

Author

Listed:
  • Ioannis APOSTOLIDIS

    (University of Bucharest, Bucharest, Romania)

  • Isabelle COICIU

    (University of Bucharest, Bucharest, Romania)

  • Ioan Florin VOICU

    (ING Tech, Bucharest, Romania)

  • Daniel Constantin DIACONU

    (Interdisciplinary Center for Advanced Studies (CISA-ICUB), University of Bucharest, Romania)

Abstract

Smart cities are increasingly integrating Internet of Things (IoT) and Artificial Intelligence (AI) technologies to collect real-time data and respond to citizens' needs. The objective of this study is to examine the impact of Green Artificial Intelligence (Green AI) on the sustainable development of smart cities, focusing on how energy-efficient algorithms and environmentally conscious machine learning systems can optimize urban operations. The research aims to demonstrate how Green AI supports the creation of climate-resilient, resource-efficient, and human-centered urban ecosystems. Building upon prior work in sustainable innovation and AI for environmental management, this study extends the discourse toward the intersection of Green AI and smart city transformation. Previous research in sustainable urban design and AI-enabled infrastructure has shown the potential of intelligent technologies to reduce emissions and improve resource utilization, yet few studies explicitly address the sustainability of the AI systems themselves. The present paper contributes to this emerging field by proposing an integrative conceptual framework grounded in theoretical review. The approach synthesizes existing literature and case evidence to analyze the role of Green AI in energy management, mobility optimization, and digital governance. The analysis suggests that Green AI has the potential to enhance predictive analytics for urban planning, support smart grid efficiency, enable adaptive traffic and waste management systems, and strengthen environmental monitoring through low-carbon computation. The implications of this study are significant for academics, policymakers, and urban developers, offering insights into how sustainable AI deployment can balance technological advancement with ecological preservation. The value of the paper lies in its holistic perspective on Green AI as both a catalyst and a constraint in smart city evolution, providing an original theoretical foundation for future empirical research and policy development in sustainable digital urbanization.

Suggested Citation

  • Ioannis APOSTOLIDIS & Isabelle COICIU & Ioan Florin VOICU & Daniel Constantin DIACONU, 2025. "Green AI power impact on development of smart cities," Smart Cities International Conference (SCIC) Proceedings, Smart-EDU Hub, Faculty of Public Administration, National University of Political Studies & Public Administration, vol. 13, pages 371-381, september.
    • Handle: RePEc:pop:procee:v:13:y:2025:p:371-381
    as

    Download full text from publisher

    File URL: https://scrd.eu/index.php/scic/article/view/880/949
    Download Restriction: no
    File URL: https://scrd.eu/index.php/scic/article/view/880
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Gloria Cicerone & Alessandra Faggian & Sandro Montresor & Francesco Rentocchini, 2023. "Regional artificial intelligence and the geography of environmental technologies: does local AI knowledge help regional green-tech specialization?," Regional Studies, Taylor & Francis Journals, vol. 57(2), pages 330-343, February.
    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. Wu, Weiwei & Zhang, Yifan, 2025. "Artificial intelligence innovation and environmental performance: Unraveling the complex roles of application and method innovation across enterprise sizes," Technological Forecasting and Social Change, Elsevier, vol. 218(C).
    2. C. Castaldi & F. Castellacci & A. Fronzetti Colladon & L. Segneri & F. Venturini, 2026. "Measuring AI innovation with trademark data," Papers 2607.18795, arXiv.org.
    3. Cicerone, Gloria & Losacker, Sebastian & Ortega-Argilés, Raquel, 2024. "Regional diversification into green and digital economic activities – The case of UK Local Authorities," Discussion Papers escoe-dp-2024-07, Economic Statistics Centre of Excellence.
    4. Sun, Zhiyao & Che, Shuai & Wang, Jie, 2024. "Deconstruct artificial intelligence's productivity impact: A new technological insight," Technology in Society, Elsevier, vol. 79(C).
    5. Herman, Kyle S. & Amortegui, Tatiana Gaitan & Griffiths, Steve, 2026. "Are AI and environmental technology innovations converging?," Technology in Society, Elsevier, vol. 85(C).
    6. Dai, Shangze & Ji, Xinde James, 2026. "Unintended carbon cost of automation technology in transforming economies: The role of capital dependence and structure change," Ecological Economics, Elsevier, vol. 244(C).
    7. Abbasiharofteh, Milad & Kriesch, Lukas, 2024. "Not all twins are identical: the digital layer of “twin” transition market applications," Papers in Innovation Studies 2024/16, Lund University, CIRCLE - Centre for Innovation Research.
    8. Stefano Basilico & Alberto Marzucchi & Sandro Montresor, 2024. "Combining digital and green technologies in regions: how to close the gap with respect to the frontier?," Papers in Evolutionary Economic Geography (PEEG) 2440, Utrecht University, Department of Human Geography and Spatial Planning, Group Economic Geography, revised Dec 2024.
    9. Saverio Barabuffi & Jacopo Cricchio & Alberto Di Minin & Guido Pialli, 2026. "The network advantage: benefits of interregional scientific knowledge spillovers on AI patenting," Papers 2609.26604, arXiv.org.
    10. Xi, Kang & Shao, Xuefeng, 2025. "Impact of AI applications on corporate green innovation," International Review of Economics & Finance, Elsevier, vol. 99(C).
    11. Scandura, Alessandra & Orsatti, Gianluca & Quatraro, Francesco & Fusillo, Fabrizio & Bergamini, Enrico, 2026. "Green diversification, global knowledge sourcing and local skill composition: Evidence from the US," Research Policy, Elsevier, vol. 55(4).
    12. Corrocher, Nicoletta & Grabner, Simone Maria & Morrison, Andrea, 2024. "Green technological diversification: The role of international linkages in leaders, followers and catching-up countries," Research Policy, Elsevier, vol. 53(4).
    13. Zhong, Wenli & Liu, Yang & Dong, Kangyin & Ni, Guohua, 2024. "Assessing the synergistic effects of artificial intelligence on pollutant and carbon emission mitigation in China," Energy Economics, Elsevier, vol. 138(C).
    14. Zhengang Zhang & Peilun Li & Liangxiong Huang & Yichen Kang, 2024. "The impact of artificial intelligence on green transformation of manufacturing enterprises: evidence from China," Economic Change and Restructuring, Springer, vol. 57(4), pages 1-36, August.
    15. Zhang, Henglong & Zhang, Yeheng & Yu, Yongwei & Ge, Liming, 2026. "Towards technology-convergent cities: How does the low-carbon economy contribute?," Structural Change and Economic Dynamics, Elsevier, vol. 77(C), pages 185-206.
    16. Emilio Colombo & Alessia De Santo & Francesco Trentini, 2025. "Let's get green: understanding green skills and jobs through online job advertisements," DISEIS - Quaderni del Dipartimento di Economia internazionale, delle istituzioni e dello sviluppo dis2503, Università Cattolica del Sacro Cuore, Dipartimento di Economia internazionale, delle istituzioni e dello sviluppo (DISEIS).
    17. Youngsam Chun & Junseok Hwang, 2025. "The Nexus of Artificial Intelligence and Green Innovation: a Cross-Density Analysis at the Country Level," Journal of the Knowledge Economy, Springer;Portland International Center for Management of Engineering and Technology (PICMET), vol. 16(1), pages 1688-1716, March.
    18. Nelli, Linnea & Virgillito, Maria Enrica & Vivarelli, Marco, 2025. "A Twin Transition or a Policy Flagship? Emergent Constellations and Dominant Blocks in Green and Digital Technologies," IZA Discussion Papers 17779, IZA Network @ LISER.
    19. Fu, Tong & Qiu, Zhaoxuan & Yang, Xiangyang & Li, Zijun, 2024. "The impact of artificial intelligence on green technology cycles in China," Technological Forecasting and Social Change, Elsevier, vol. 209(C).
    20. David Audretsch & Maksim Belitski & Antje Fiedler, 2026. "The knowledge spillover theory of entrepreneurship and innovation: taking stock and new directions," The Journal of Technology Transfer, Springer, vol. 51(4), pages 2117-2140, August.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;

    JEL classification:

    • O35 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Social Innovation

    Statistics

    Access and download statistics

    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:pop:procee:v:13:y:2025:p:371-381. 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: Professor Catalin Vrabie (email available below). General contact details of provider: https://edirc.repec.org/data/fasnsro.html .

    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.