IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v17y2025i5p2006-d1600420.html
   My bibliography  Save this article

Artificial Intelligence and Social Well-Being in the Yellow River Basin: A Cultural Lag Theory Perspective

Author

Listed:
  • Zhaoxin Song

    (School of Economic Management, Inner Mongolia University of Science and Technology, Baotou 014020, China)

  • Yongfeng Duan

    (School of Economic Management, Inner Mongolia University of Science and Technology, Baotou 014020, China)

  • Guanying Wang

    (College of Management and Economics, Tianjin University, Tianjin 300072, China)

  • Shuoxun Cheng

    (School of International Economics and Trade, Nanjing University of Finance and Economics, Nanjing 210003, China)

Abstract

Amid comprehensive reforms, artificial intelligence (AI) has emerged as a vital force in solving people’s problems and enhancing quality of life. Yet, theoretical inquiries into the mechanisms by which AI influences social well-being remain limited. Drawing upon cultural lag theory, this study constructs a social well-being index system based on the Gini coefficient objective weighting method. By integrating a moderated mediation model with a spatial econometric model, it examines the mechanisms and impacts of artificial intelligence on social well-being. The findings reveal that AI induces multiple cultural lags and exerts a U-shaped impact on social well-being. AI enhances well-being through the channels of employment opportunities, human capital, and green innovation, while digital inclusion and foreign direct investment (FDI) further reinforce this relationship. Additionally, AI generates spatial spillover effects on social well-being, and the region’s well-being landscape exhibits convergence. However, both digital inclusion and FDI negatively moderate the convergence process, slowing its overall pace. These insights provide substantial practical guidance for crafting informed policies aimed at elevating public well-being.

Suggested Citation

  • Zhaoxin Song & Yongfeng Duan & Guanying Wang & Shuoxun Cheng, 2025. "Artificial Intelligence and Social Well-Being in the Yellow River Basin: A Cultural Lag Theory Perspective," Sustainability, MDPI, vol. 17(5), pages 1-27, February.
    • Handle: RePEc:gam:jsusta:v:17:y:2025:i:5:p:2006-:d:1600420
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/17/5/2006/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/17/5/2006/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Yu, Chin-Hsien & Wu, Xiuqin & Zhang, Dayong & Chen, Shi & Zhao, Jinsong, 2021. "Demand for green finance: Resolving financing constraints on green innovation in China," Energy Policy, Elsevier, vol. 153(C).
    2. Daron Acemoglu & David Autor & Jonathon Hazell & Pascual Restrepo, 2022. "Artificial Intelligence and Jobs: Evidence from Online Vacancies," Journal of Labor Economics, University of Chicago Press, vol. 40(S1), pages 293-340.
    3. Zhang, Dongyang, 2024. "The pathway to curb greenwashing in sustainable growth: The role of artificial intelligence," Energy Economics, Elsevier, vol. 133(C).
    4. Zhou, Wei & Zhuang, Yan & Chen, Yan, 2024. "How does artificial intelligence affect pollutant emissions by improving energy efficiency and developing green technology," Energy Economics, Elsevier, vol. 131(C).
    5. Gruetzemacher, Ross & Paradice, David & Lee, Kang Bok, 2020. "Forecasting extreme labor displacement: A survey of AI practitioners," Technological Forecasting and Social Change, Elsevier, vol. 161(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. Cao, Qingfeng & Chi, Chuenyu & Shan, Junhui, 2025. "Can artificial intelligence technology reduce carbon emissions? A global perspective," Energy Economics, Elsevier, vol. 143(C).
    2. Huang, Yujie & Liu, Shucheng & Gan, Jiawu & Liu, Baoliu & Wu, Yuxi, 2024. "How does the construction of new generation of national AI innovative development pilot zones drive enterprise ESG development? Empirical evidence from China," Energy Economics, Elsevier, vol. 140(C).
    3. Niu, Xiaotong & Lin, Changao & He, Shanshan & Yang, Youcai, 2025. "Artificial intelligence and enterprise pollution emissions: From the perspective of energy transition," Energy Economics, Elsevier, vol. 144(C).
    4. Jiao, Anqi & Lu, Juntai & Ren, Honglin & Wei, Jia, 2024. "The role of AI capabilities in environmental management: Evidence from USA firms," Energy Economics, Elsevier, vol. 134(C).
    5. Wu, Yongqiu & Lin, Zhiwei & Zhang, Qingcui & Wang, Wei, 2024. "Artificial intelligence, wage dynamics, and inequality: Empirical evidence from Chinese listed firms," International Review of Economics & Finance, Elsevier, vol. 96(PC).
    6. Qadri, Hussain Mohi ud Din & Ali, Hassnian & Abideen, Zain ul & Jafar, Ahmad, 2024. "Mapping the Evolution of Green Finance Research and Development in Emerging Green Economies," Resources Policy, Elsevier, vol. 91(C).
    7. Guo, Shu & Zhang, ZhongXiang, 2023. "Green credit policy and total factor productivity: Evidence from Chinese listed companies," Energy Economics, Elsevier, vol. 128(C).
    8. Andrei Ternikov, 2023. "Skill preferences in job postings," Economics Bulletin, AccessEcon, vol. 43(4), pages 1928-1943.
    9. Gao, Jie & Li, Zhizhuo & Nguyen, Thithuha & Zhang, Wentao, 2025. "Digital transformation and enterprise employment," International Review of Economics & Finance, Elsevier, vol. 99(C).
    10. Lin, Boqiang & Xu, Chongchong, 2024. "The effects of industrial robots on firm energy intensity: From the perspective of technological innovation and electrification," Technological Forecasting and Social Change, Elsevier, vol. 203(C).
    11. Tan, Xiujie & Yan, Yaxue & Dong, Yuyang, 2022. "Peer effect in green credit induced green innovation: An empirical study from China's Green Credit Guidelines," Resources Policy, Elsevier, vol. 76(C).
    12. Kirtac, Kemal & Germano, Guido, 2024. "Sentiment trading with large language models," Finance Research Letters, Elsevier, vol. 62(PB).
    13. Yongjian Wang & Fei Wang & Wenbo Li, 2023. "Effects of the Carbon Credit Policy on the Capital-Constrained Manufacturer’s Remanufacturing and Emissions Decisions," IJERPH, MDPI, vol. 20(5), pages 1-17, February.
    14. Alexander Bick & Adam Blandin & David Deming, 2023. "The Rapid Adoption of Generative AI," On the Economy 98843, Federal Reserve Bank of St. Louis.
    15. 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.
    16. Marydas, Sneha & Mathew, Nanditha & De Marzo, Giordano & Pietrobelli, Carlo, 2025. "Digital Technologies, Hiring, Training, and Firm Outcomes," MERIT Working Papers 2025-004, United Nations University - Maastricht Economic and Social Research Institute on Innovation and Technology (MERIT).
    17. Wang, Yafei & Shi, Ming & Zhao, Zihan & Liu, Junnan & Zhang, Shiqiu, 2025. "How does green finance improve the total factor energy efficiency? Capturing the mediating role of green management innovation and embodied technological progress," Energy Economics, Elsevier, vol. 142(C).
    18. Mühlemann, Samuel, 2024. "AI Adoption and Workplace Training," IZA Discussion Papers 17367, Institute of Labor Economics (IZA).
    19. Enrico Maria Fenoaltea & Dario Mazzilli & Aurelio Patelli & Angelica Sbardella & Andrea Tacchella & Andrea Zaccaria & Marco Trombetti & Luciano Pietronero, 2024. "Follow the money: a startup-based measure of AI exposure across occupations, industries and regions," Papers 2412.04924, arXiv.org, revised Dec 2024.
    20. Naomi Hausman & Oren Rigbi & Sarit Weisburd, 2025. "Generative AI’s Impact on Student Achievement and Implications for Worker Productivity," CESifo Working Paper Series 11843, CESifo.

    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:5:p:2006-:d:1600420. 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.