IDEAS home Printed from https://ideas.repec.org/a/spr/jknowl/v15y2024i3d10.1007_s13132-023-01506-3.html
   My bibliography  Save this article

Artificial Intelligence Drives the Coordinated Development of Green Finance and the Real Economy: Empirical Evidence from Chinese Provincial Level

Author

Listed:
  • Gangdong Peng

    (Huazhong University of Science and Technology)

  • Minchun Han

    (Huazhong University of Science and Technology)

  • Hankun Yuan

    (Party School of CPC Jiangsu Provincial Committee)

Abstract

This article constructs the subsystems of modern finance and the real economy by selecting indicators. Using the method of coupling coordination analysis, it measures the coordination status of 30 provinces (municipalities, autonomous regions) in China. The findings show that only 7 provinces are in a coordinated state, while the remaining 23 provinces are in a disordered state. To address this general lack of coordination, we analyze the direct and indirect mechanisms of artificial intelligence (AI) based on its principles of operation and conduct empirical tests. In the empirical analysis, we measure the level of AI innovation development using patent data from the Qizhidao Database and the level of AI technology application using data on industrial robot penetration from the International Federation of Robotics. We employ panel data at the provincial level from 2010 to 2019 and conduct least squares regression tests on the direct and indirect effects of AI. The results are significant and pass robustness tests, endogeneity tests, and heterogeneity tests. The research findings indicate that AI can directly promote the coordinated development of green finance and the real economy by addressing information asymmetry and other issues. It can also indirectly promote their coordinated development by improving institutional levels, technological levels, human capital levels, and government governance capabilities. Furthermore, subgroup analysis reveals that the effects are more pronounced in regions with higher investment in AI research and development, a larger number of AI talents, and better AI infrastructure. These results are consistent with the basic logical understanding. Finally, based on the research conclusions, we propose four policy recommendations to increase the application of AI in the fields of economy and finance.

Suggested Citation

  • Gangdong Peng & Minchun Han & Hankun Yuan, 2024. "Artificial Intelligence Drives the Coordinated Development of Green Finance and the Real Economy: Empirical Evidence from Chinese Provincial Level," Journal of the Knowledge Economy, Springer;Portland International Center for Management of Engineering and Technology (PICMET), vol. 15(3), pages 10257-10295, September.
    • Handle: RePEc:spr:jknowl:v:15:y:2024:i:3:d:10.1007_s13132-023-01506-3
    DOI: 10.1007/s13132-023-01506-3
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s13132-023-01506-3
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s13132-023-01506-3?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    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. Erik Brynjolfsson & Tom Mitchell & Daniel Rock, 2018. "What Can Machines Learn, and What Does It Mean for Occupations and the Economy?," AEA Papers and Proceedings, American Economic Association, vol. 108, pages 43-47, May.
    3. William D. Nordhaus, 2021. "Are We Approaching an Economic Singularity? Information Technology and the Future of Economic Growth," American Economic Journal: Macroeconomics, American Economic Association, vol. 13(1), pages 299-332, January.
    4. Philippe Aghion & Benjamin F. Jones & Charles I. Jones, 2018. "Artificial Intelligence and Economic Growth," NBER Chapters, in: The Economics of Artificial Intelligence: An Agenda, pages 237-282, National Bureau of Economic Research, Inc.
    5. Yin, Xiuling & Xu, Zhaoran, 2022. "An empirical analysis of the coupling and coordinative development of China's green finance and economic growth," Resources Policy, Elsevier, vol. 75(C).
    6. Nagy K. Hanna, 2020. "Assessing the digital economy: aims, frameworks, pilots, results, and lessons," Journal of Innovation and Entrepreneurship, Springer, vol. 9(1), pages 1-16, December.
    7. 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.
    8. Lee, Chi-Chuan & Lee, Chien-Chiang, 2022. "How does green finance affect green total factor productivity? Evidence from China," Energy Economics, Elsevier, vol. 107(C).
    9. Nana Liu & Chuanzhe Liu & Yufei Xia & Yi Ren & Jinzhi Liang, 2020. "Examining the Coordination Between Green Finance and Green Economy Aiming for Sustainable Development: A Case Study of China," Sustainability, MDPI, vol. 12(9), pages 1-26, May.
    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. Lee, Chien-Chiang & Wang, Fuhao & Lou, Runchi & Wang, Keying, 2023. "How does green finance drive the decarbonization of the economy? Empirical evidence from China," Renewable Energy, Elsevier, vol. 204(C), pages 671-684.
    2. Lu, Yuchen & Gao, Yuqiang & Zhang, Yu & Wang, Junrong, 2022. "Can the green finance policy force the green transformation of high-polluting enterprises? A quasi-natural experiment based on “Green Credit Guidelines”," Energy Economics, Elsevier, vol. 114(C).
    3. Changfei Nie & Yujie Yao & Yuan Feng, 2024. "The impact of green finance on economic growth: Evidence from the green finance reform and innovation pilot zone," American Journal of Economics and Sociology, Wiley Blackwell, vol. 83(4), pages 709-736, September.
    4. Lei Nie & Purong Chen & Xiuli Liu & Qinqin Shi & Jing Zhang, 2022. "Coupling and Coordinative Development of Green Finance and Industrial-Structure Optimization in China: Spatial-Temporal Difference and Driving Factors," IJERPH, MDPI, vol. 19(17), pages 1-22, September.
    5. Li, Songran & Shao, Qinglong, 2022. "Greening the finance for climate mitigation: An ARDL–ECM approach," Renewable Energy, Elsevier, vol. 199(C), pages 1469-1481.
    6. Chen, Chuanglian & Wang, Shudan & Yao, Shujie & Lin, Yuting, 2023. "Does digital transformation increase the labor income share? From a perspective of resources reallocation," Economic Modelling, Elsevier, vol. 128(C).
    7. Qian, Cheng & Zhu, Chun & Huang, Duen-Huang & Zhang, Shangfeng, 2023. "Examining the influence mechanism of artificial intelligence development on labor income share through numerical simulations," Technological Forecasting and Social Change, Elsevier, vol. 188(C).
    8. Jing Sun & Ningning Zhai & Jichao Miao & Huaping Sun, 2022. "Can Green Finance Effectively Promote the Carbon Emission Reduction in “Local-Neighborhood” Areas?—Empirical Evidence from China," Agriculture, MDPI, vol. 12(10), pages 1-13, September.
    9. Lingjun Guo & Wenyu Tan, 2024. "Relationship between green credit and high-quality development of green and green economy—"pull effect" or "capricorn effect"," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 26(10), pages 26449-26473, October.
    10. 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.
    11. Guo, Shu & Zhang, ZhongXiang, 2023. "Green credit policy and total factor productivity: Evidence from Chinese listed companies," Energy Economics, Elsevier, vol. 128(C).
    12. 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.
    13. 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.
    14. 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.
    15. Wang, Jiaxin & Zhao, Mu & Huang, Xiang & Song, Zilong & Sun, Di, 2024. "Supply chain diffusion mechanisms for AI applications: A perspective on audit pricing," International Review of Financial Analysis, Elsevier, vol. 93(C).
    16. Wang, Mei Ling, 2023. "Effects of the green finance policy on the green innovation efficiency of the manufacturing industry: A difference-in-difference model," Technological Forecasting and Social Change, Elsevier, vol. 189(C).
    17. Cao, Sean & Jiang, Wei & Wang, Junbo & Yang, Baozhong, 2024. "From Man vs. Machine to Man + Machine: The art and AI of stock analyses," Journal of Financial Economics, Elsevier, vol. 160(C).
    18. Jia, Junsheng & He, Xiaoyu & Zhu, Taiyu & Zhang, Eryu, 2023. "Does green finance reform promote corporate green innovation? Evidence from China," Pacific-Basin Finance Journal, Elsevier, vol. 82(C).
    19. Zhang, Lixia & Sun, Huaping & Pu, Tianlong & Sun, Hui & Chen, Zhenling, 2024. "Do green finance and hi-tech innovation facilitate sustainable development? Evidence from the Yangtze River Economic Belt," Economic Analysis and Policy, Elsevier, vol. 81(C), pages 1430-1442.
    20. Andreas Irmen, 2021. "Automation, growth, and factor shares in the era of population aging," Journal of Economic Growth, Springer, vol. 26(4), pages 415-453, December.

    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:spr:jknowl:v:15:y:2024:i:3:d:10.1007_s13132-023-01506-3. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.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.