IDEAS home Printed from https://ideas.repec.org/a/kap/compec/v65y2025i3d10.1007_s10614-024-10591-8.html
   My bibliography  Save this article

Determinants and Pathways for Inclusive Growth in China: Investigation Based on Artificial Intelligence (AI) Algorithm

Author

Listed:
  • Shuangshuang Fan

    (Wenzhou University of Technology)

  • Yichao Li

    (Hainan University)

  • William Mbanyele

    (Shandong University)

  • Xiufeng Lai

    (China University of Mining and Technology-Beijing)

Abstract

Can artificial intelligence (AI) algorithms help policymakers in their decisions on inclusive growth? In this study, we introduce artificial intelligence algorithms to calibrate China's inclusive growth determinants. We uncover various factors that significantly influence inclusive growth using machine learning forecasts. Furthermore, our results using best practice methods outperform findings from traditional regression-based strategies on other dimensions, which miss non-linear interactions in their estimations. However, we observe that when the actual value of the inclusive growth index is too large, the accuracy of the machine learning model is diminished. Meanwhile, the results of heterogeneity analysis reveal that the determinants of inclusive growth in cities with different region and different marketization level are distinct. In addition, we adopt the scenario simulation and prediction approach to reveal the best policy measure to promote inclusive growth in China. Our findings indicate that machine learning holds promise for understanding how inclusive growth can be achieved and can assist real-world economies in enhancing inclusive growth.

Suggested Citation

  • Shuangshuang Fan & Yichao Li & William Mbanyele & Xiufeng Lai, 2025. "Determinants and Pathways for Inclusive Growth in China: Investigation Based on Artificial Intelligence (AI) Algorithm," Computational Economics, Springer;Society for Computational Economics, vol. 65(3), pages 1231-1264, March.
    • Handle: RePEc:kap:compec:v:65:y:2025:i:3:d:10.1007_s10614-024-10591-8
    DOI: 10.1007/s10614-024-10591-8
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10614-024-10591-8
    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/s10614-024-10591-8?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. Boikos, Spyridon & Panagiotidis, Theodore & Voucharas, Georgios, 2022. "Financial development, reforms and growth," Economic Modelling, Elsevier, vol. 108(C).
    2. Shihao Gu & Bryan Kelly & Dacheng Xiu, 2020. "Empirical Asset Pricing via Machine Learning," Review of Finance, European Finance Association, vol. 33(5), pages 2223-2273.
    3. Zhao, Jun & Jiang, Qingzhe & Dong, Xiucheng & Dong, Kangyin & Jiang, Hongdian, 2022. "How does industrial structure adjustment reduce CO2 emissions? Spatial and mediation effects analysis for China," Energy Economics, Elsevier, vol. 105(C).
    4. Ghoddusi, Hamed & Creamer, Germán G. & Rafizadeh, Nima, 2019. "Machine learning in energy economics and finance: A review," Energy Economics, Elsevier, vol. 81(C), pages 709-727.
    5. Zhang, Bangbang & Li, Xian & Chen, Haibin & Niu, Wenhao & Kong, Xiangbin & Yu, Qiang & Zhao, Minjuan & Xia, Xianli, 2022. "Identifying opportunities to close yield gaps in China by use of certificated cultivars to estimate potential productivity," Land Use Policy, Elsevier, vol. 117(C).
    6. Molero-Simarro, Ricardo, 2017. "Inequality in China revisited. The effect of functional distribution of income on urban top incomes, the urban-rural gap and the Gini index, 1978–2015," China Economic Review, Elsevier, vol. 42(C), pages 101-117.
    7. Shihao Gu & Bryan Kelly & Dacheng Xiu, 2020. "Empirical Asset Pricing via Machine Learning," The Review of Financial Studies, Society for Financial Studies, vol. 33(5), pages 2223-2273.
    8. Christian-Lambert Nguena & Fulbert Tchana Tchana & Albert G. Zeufack, 2021. "Housing Finance and Inclusive Growth in Africa: Benchmarking, Determinants and Effects," International Economic Journal, Taylor & Francis Journals, vol. 35(2), pages 259-287, April.
    9. Akbari, Amir & Ng, Lilian & Solnik, Bruno, 2021. "Drivers of economic and financial integration: A machine learning approach," Journal of Empirical Finance, Elsevier, vol. 61(C), pages 82-102.
    10. Ofori, Isaac Kwesi & Asongu, Simplice A., 2021. "ICT Diffusion, Foreign Direct Investment and Inclusive Growth in Sub-Saharan Africa," MPRA Paper 107757, University Library of Munich, Germany.
    11. Jana, Rabin K. & Ghosh, Indranil & Das, Debojyoti & Dutta, Anupam, 2021. "Determinants of electronic waste generation in Bitcoin network: Evidence from the machine learning approach," Technological Forecasting and Social Change, Elsevier, vol. 173(C).
    12. Sharma, Ashutosh & Chauhan, Ranchan & Singh, Tej & Kumar, Anil & Kumar, Raj & Kumar, Anil & Sethi, Muneesh, 2017. "Optimizing discrete V obstacle parameters using a novel Entropy-VIKOR approach in a solar air flow channel," Renewable Energy, Elsevier, vol. 106(C), pages 310-320.
    13. Goel, Raj Kumar & Vishnoi, Shweta, 2022. "Urbanization and sustainable development for inclusiveness using ICTs," Telecommunications Policy, Elsevier, vol. 46(6).
    14. Chen, Shengqi & Zhang, Hong, 2021. "Does digital finance promote manufacturing servitization: Micro evidence from China," International Review of Economics & Finance, Elsevier, vol. 76(C), pages 856-869.
    15. Wang, Wei & Xiao, Weiwei & Bai, Caiquan, 2022. "Can renewable energy technology innovation alleviate energy poverty? Perspective from the marketization level," Technology in Society, Elsevier, vol. 68(C).
    16. Bollaert, Helen & Lopez-de-Silanes, Florencio & Schwienbacher, Armin, 2021. "Fintech and access to finance," Journal of Corporate Finance, Elsevier, vol. 68(C).
    17. Amad Ullah Khan & Saad Salman & Khan Muhammad & Mudassar Habib, 2022. "Modelling Coal Dust Explosibility of Khyber Pakhtunkhwa Coal Using Random Forest Algorithm," Energies, MDPI, vol. 15(9), pages 1-20, April.
    18. Liqing Huang & Bangzhu Zhu & Ping Wang & Julien Chevallier, 2022. "Energy out-of-poverty and inclusive growth: Evidence from the China health and nutrition survey," Post-Print halshs-04250294, HAL.
    19. Huang, Liqing & Zhu, Bangzhu & Wang, Ping & Chevallier, Julien, 2022. "Energy out-of-poverty and inclusive growth: Evidence from the China health and nutrition survey," Structural Change and Economic Dynamics, Elsevier, vol. 60(C), pages 344-352.
    20. Ahmad, Sohail & Puppim de Oliveira, Jose A., 2016. "Determinants of urban mobility in India: Lessons for promoting sustainable and inclusive urban transportation in developing countries," Transport Policy, Elsevier, vol. 50(C), pages 106-114.
    21. Yang, Lisha & Ni, Mengying, 2022. "Is financial development beneficial to improve the efficiency of green development? Evidence from the “Belt and Road” countries," Energy Economics, Elsevier, vol. 105(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. Fan, Shuangshuang & Wei, Yuetong & Niu, Xiao & Balezentis, Tomas & Agnusdei, Leonardo, 2023. "Can FinTech development pave the way for a transition towards inclusive growth: Evidence from an emerging economy," Structural Change and Economic Dynamics, Elsevier, vol. 67(C), pages 439-458.
    2. Chunhua Xin & Shuangshuang Fan & Zihao Guo, 2024. "Can digital finance promote inclusive growth to meet sustainable development in China? A machine learning approach," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 26(10), pages 26647-26677, October.
    3. Evangelos Liaras & Michail Nerantzidis & Antonios Alexandridis, 2024. "Machine learning in accounting and finance research: a literature review," Review of Quantitative Finance and Accounting, Springer, vol. 63(4), pages 1431-1471, November.
    4. Gang Kou & Yang Lu, 2025. "FinTech: a literature review of emerging financial technologies and applications," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 11(1), pages 1-34, December.
    5. Nguyen, Quyen & Diaz-Rainey, Ivan & Kuruppuarachchi, Duminda, 2021. "Predicting corporate carbon footprints for climate finance risk analyses: A machine learning approach," Energy Economics, Elsevier, vol. 95(C).
    6. Lei Xu & Qian Liu & Bin Li & Chen Ma, 2022. "Fintech business and firm access to bank loans," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 62(4), pages 4381-4421, December.
    7. Zhang, Hongwei & Wang, Wentao & Niu, Zibo, 2024. "Geopolitical risks and crude oil futures volatility: Evidence from machine learning," Resources Policy, Elsevier, vol. 98(C).
    8. Muneer M. Alshater & Ilias Kampouris & Hazem Marashdeh & Osama F. Atayah & Hasanul Banna, 2025. "Early warning system to predict energy prices: the role of artificial intelligence and machine learning," Annals of Operations Research, Springer, vol. 345(2), pages 1297-1333, February.
    9. Yao, Haixiang & Xia, Shenghao & Liu, Hao, 2022. "Six-factor asset pricing and portfolio investment via deep learning: Evidence from Chinese stock market," Pacific-Basin Finance Journal, Elsevier, vol. 76(C).
    10. Feng Ma & Xinjie Lu & Bo Zhu, 2025. "Uncertainty and fluctuation in crude oil price: evidence from machine learning models," Annals of Operations Research, Springer, vol. 345(2), pages 725-755, February.
    11. Lu, Xinjie & Ma, Feng & Xu, Jin & Zhang, Zehui, 2022. "Oil futures volatility predictability: New evidence based on machine learning models11All the authors contribute to the paper equally," International Review of Financial Analysis, Elsevier, vol. 83(C).
    12. Huang, Hongyun & Mbanyele, William & Fan, Shuangshuang & Zhao, Xin, 2022. "Digital financial inclusion and energy-environment performance: What can learn from China," Structural Change and Economic Dynamics, Elsevier, vol. 63(C), pages 342-366.
    13. Sun, Chuanwang & Xu, Mengjie & Wang, Bo, 2024. "Deep learning: Spatiotemporal impact of digital economy on energy productivity," Renewable and Sustainable Energy Reviews, Elsevier, vol. 199(C).
    14. Michalski, Lachlan & Low, Rand Kwong Yew, 2024. "Determinants of corporate credit ratings: Does ESG matter?," International Review of Financial Analysis, Elsevier, vol. 94(C).
    15. Andrii Babii & Ryan T. Ball & Eric Ghysels & Jonas Striaukas, 2024. "Panel data nowcasting: The case of price–earnings ratios," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 39(2), pages 292-307, March.
    16. Bakalli, Gaetan & Guerrier, Stéphane & Scaillet, Olivier, 2023. "A penalized two-pass regression to predict stock returns with time-varying risk premia," Journal of Econometrics, Elsevier, vol. 237(2).
    17. Philippe Goulet Coulombe & Maxime Leroux & Dalibor Stevanovic & Stéphane Surprenant, 2022. "How is machine learning useful for macroeconomic forecasting?," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(5), pages 920-964, August.
    18. Wang, Yudong & Hao, Xianfeng, 2022. "Forecasting the real prices of crude oil: A robust weighted least squares approach," Energy Economics, Elsevier, vol. 116(C).
    19. Tobias Götze & Marc Gürtler & Eileen Witowski, 2020. "Improving CAT bond pricing models via machine learning," Journal of Asset Management, Palgrave Macmillan, vol. 21(5), pages 428-446, September.
    20. Wen, Danyan & Liu, Li & Wang, Yudong & Zhang, Yaojie, 2022. "Forecasting crude oil market returns: Enhanced moving average technical indicators," Resources Policy, Elsevier, vol. 76(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:kap:compec:v:65:y:2025:i:3:d:10.1007_s10614-024-10591-8. 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.