IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v16y2024i8p3200-d1373833.html
   My bibliography  Save this article

The Impact of Artificial Intelligence Development on Urban Energy Efficiency—Based on the Perspective of Smart City Policy

Author

Listed:
  • Xiangyi Li

    (Hubei Recycling Economy Development Research Center, Hubei University of Technology, Wuhan 430068, China)

  • Qing Wang

    (Hubei Recycling Economy Development Research Center, Hubei University of Technology, Wuhan 430068, China)

  • Ying Tang

    (School of Business Administration, Zhongnan University of Economics and Law, Wuhan 430073, China)

Abstract

China’s economy is stepping into a new stage of high-quality development. The shift not only marks the optimization and upgrading of the economic structure, but also reflects the in-depth implementation of the concept of sustainable development. In this context, the development of AI technology is playing an important role in balancing economic growth and ecological protection with its unique advantages. This paper empirically studied the impact of AI development on urban energy efficiency by constructing panel data for 282 prefecture-level cities from 2006 to 2019 and then using the super-efficiency SBM model based on non-expected outputs to evaluate the urban energy efficiency indicators of prefecture-level cities. It was discovered that the development of AI had a key influence on increasing urban energy efficiency and the optimization of the energy structure by speeding up green technology innovation and digital economy development, which in turn improved urban energy efficiency. In terms of heterogeneity analysis, AI development had a greater impact on urban energy efficiency in the eastern region, which has higher levels of human capital, financial independence, and government intervention. This study combined the smart city pilot policy with a multi-period DID model, based on the treatment of endogeneity issues, in order to perform a parallel trend test and investigate further the effects of policy implementation on the advancement of AI in the context of improving urban energy efficiency. Accordingly, to achieve green and sustainable urban development, the relevant government departments should increase funding for AI research and development, pay attention to the introduction and cultivation of professionals, establish a platform for international exchanges and cooperation between AI and energy management, and continue to advocate for the pilot development of smart cities to increase urban energy efficiency.

Suggested Citation

  • Xiangyi Li & Qing Wang & Ying Tang, 2024. "The Impact of Artificial Intelligence Development on Urban Energy Efficiency—Based on the Perspective of Smart City Policy," Sustainability, MDPI, vol. 16(8), pages 1-22, April.
    • Handle: RePEc:gam:jsusta:v:16:y:2024:i:8:p:3200-:d:1373833
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/16/8/3200/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/16/8/3200/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Li, Juan & Ma, Shaoqi & Qu, Yi & Wang, Jiamin, 2023. "The impact of artificial intelligence on firms’ energy and resource efficiency: Empirical evidence from China," Resources Policy, Elsevier, vol. 82(C).
    2. Daron Acemoglu & Pascual Restrepo, 2018. "The Race between Man and Machine: Implications of Technology for Growth, Factor Shares, and Employment," American Economic Review, American Economic Association, vol. 108(6), pages 1488-1542, June.
    3. Babina, Tania & Fedyk, Anastassia & He, Alex & Hodson, James, 2024. "Artificial intelligence, firm growth, and product innovation," Journal of Financial Economics, Elsevier, vol. 151(C).
    4. Liu, Liang & Yang, Kun & Fujii, Hidemichi & Liu, Jun, 2021. "Artificial intelligence and energy intensity in China’s industrial sector: Effect and transmission channel," Economic Analysis and Policy, Elsevier, vol. 70(C), pages 276-293.
    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. Chen, Yang & Cheng, Liang & Lee, Chien-Chiang, 2022. "How does the use of industrial robots affect the ecological footprint? International evidence," Ecological Economics, Elsevier, vol. 198(C).
    2. Dennis C. Hutschenreiter & Tommaso Santini & Eugenia Vella, 2022. "Automation and sectoral reallocation," SERIEs: Journal of the Spanish Economic Association, Springer;Spanish Economic Association, vol. 13(1), pages 335-362, May.
    3. Josten, Cecily & Lordan, Grace, 2019. "Robots at Work: Automatable and Non Automatable Jobs," IZA Discussion Papers 12520, Institute of Labor Economics (IZA).
    4. Basso, Henrique S. & Jimeno, Juan F., 2021. "From secular stagnation to robocalypse? Implications of demographic and technological changes," Journal of Monetary Economics, Elsevier, vol. 117(C), pages 833-847.
    5. Kyoji Fukao & Cristiano Perugini, 2021. "The Long‐Run Dynamics of the Labor Share in Japan," Review of Income and Wealth, International Association for Research in Income and Wealth, vol. 67(2), pages 445-480, June.
    6. Matteo G. Richiardi & Luis Valenzuela, 2024. "Firm heterogeneity and the aggregate labour share," LABOUR, CEIS, vol. 38(1), pages 66-101, March.
    7. Qiansheng Gong & Xiangyu Wang & Xi Tang, 2023. "How Can the Development of Digital Economy Empower Green Transformation and Upgrading of the Manufacturing Industry?—A Quasi-Natural Experiment Based on the National Big Data Comprehensive Pilot Zone ," Sustainability, MDPI, vol. 15(11), pages 1-19, May.
    8. Daniele Angelini, 2023. "Aging Population and Technology Adoption," Working Paper Series of the Department of Economics, University of Konstanz 2023-01, Department of Economics, University of Konstanz.
    9. Huang, Siyu & Shi, Yi & Chen, Qinghua & Li, Xiaomeng, 2022. "The growth path of high-tech industries: Statistical laws and evolution demands," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 603(C).
    10. Siliang Tong & Nan Jia & Xueming Luo & Zheng Fang, 2021. "The Janus face of artificial intelligence feedback: Deployment versus disclosure effects on employee performance," Strategic Management Journal, Wiley Blackwell, vol. 42(9), pages 1600-1631, September.
    11. Stephan Brunow & Stefanie Lösch & Ostap Okhrin, 2022. "Labor market tightness and individual wage growth: evidence from Germany," Journal for Labour Market Research, Springer;Institute for Employment Research/ Institut für Arbeitsmarkt- und Berufsforschung (IAB), vol. 56(1), pages 1-21, December.
    12. Engberg, Erik & Görg, Holger & Lodefalk, Magnus & Javed, Farrukh & Längkvist, Martin & Monteiro, Natália & Kyvik Nordås, Hildegunn & Pulito, Giuseppe & Schroeder, Sarah & Tang, Aili, 2023. "AI Unboxed and Jobs: A Novel Measure and Firm-Level Evidence from Three Countries," Ratio Working Papers 370, The Ratio Institute.
    13. Akaev, Askar & Devezas, Tessaleno & Ichkitidze, Yuri & Sarygulov, Askar, 2021. "Forecasting the labor intensity and labor income share for G7 countries in the digital age," Technological Forecasting and Social Change, Elsevier, vol. 167(C).
    14. Cristiano CODAGNONE & Giovanni LIVA & Egidijus BARCEVICIUS & Gianluca MISURACA & Luka KLIMAVICIUTE & Michele BENEDETTI & Irene VANINI & Giancarlo VECCHI & Emily RYEN GLOINSON & Katherine STEWART & Sti, 2020. "Assessing the impacts of digital government transformation in the EU: Conceptual framework and empirical case studies," JRC Research Reports JRC120865, Joint Research Centre.
    15. Fabian Stephany & Ole Teutloff, 2022. "What is the Price of a Skill? The Value of Complementarity," Papers 2210.01535, arXiv.org, revised Nov 2023.
    16. Philippe Aghion & Céline Antonin & Simon Bunel & Xavier Jaravel, 2020. "What Are the Labor and Product Market Effects of Automation? New Evidence from France," SciencePo Working papers Main hal-03403062, HAL.
    17. Ezra Oberfield & Devesh Raval, 2021. "Micro Data and Macro Technology," Econometrica, Econometric Society, vol. 89(2), pages 703-732, March.
    18. Hua Zhang & Qiwang Zhang, 2023. "How Does Digital Transformation Facilitate Enterprise Total Factor Productivity? The Multiple Mediators of Supplier Concentration and Customer Concentration," Sustainability, MDPI, vol. 15(3), pages 1-18, January.
    19. Chiara Cimini & David Romero & Roberto Pinto & Sergio Cavalieri, 2023. "Task Classification Framework and Job-Task Analysis Method for Understanding the Impact of Smart and Digital Technologies on the Operators 4.0 Job Profiles," Sustainability, MDPI, vol. 15(5), pages 1-28, February.
    20. Sharon Belenzon & Victor Manuel Bennett & Andrea Patacconi, 2019. "Flexible Production and Entry: Institutional, Technological, and Organizational Determinants," Strategy Science, INFORMS, vol. 4(3), pages 193-216, September.

    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:16:y:2024:i:8:p:3200-:d:1373833. 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.