IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v16y2024i3p1260-d1331832.html
   My bibliography  Save this article

How Does Artificial Intelligence Impact Green Development? Evidence from China

Author

Listed:
  • Mingyue Chen

    (School of Economics, Lanzhou University, Lanzhou 730000, China)

  • Shuting Wang

    (School of Economics, Lanzhou University, Lanzhou 730000, China)

  • Xiaowen Wang

    (School of Economics, Lanzhou University, Lanzhou 730000, China)

Abstract

Artificial intelligence not only changes the production methods of traditional industries but also provides an important opportunity to decouple industrial development from environmental degradation and promote green economic growth. In order to further explore the green value of AI, this paper constructs an indicator of industrial robot penetration at the regional level, based on the idea of Bartik’s instrumental variable, and measures green development efficiency using the improved Super-SBM model. Based on a comprehensive explanation of the influence mechanism, a spatial measurement model and mediating effect model are constructed to test the spatial spillover effect and transmission mechanism between AI and green development. This study shows that (1) there is a significant inverted U shape in the impact of AI on green development; (2) the heterogeneity analysis finds that the structural dividend of AI is more obvious in capital-intensive and technology-intensive areas, which can more fully release its empowering effect on green development; (3) AI can not only directly affect green development but also indirectly affect green development by promoting green technology innovation and optimizing industrial structures, etc.; (4) AI has a significant inverted U-shaped spatial spillover effect on green development, and the development of local AI has a radiation-driven effect on the green development performance of its spatially related areas. The research methodology of this paper can be used for future research, and the results could provide support for the formulation of regional AI applications and green development policies.

Suggested Citation

  • Mingyue Chen & Shuting Wang & Xiaowen Wang, 2024. "How Does Artificial Intelligence Impact Green Development? Evidence from China," Sustainability, MDPI, vol. 16(3), pages 1-23, February.
    • Handle: RePEc:gam:jsusta:v:16:y:2024:i:3:p:1260-:d:1331832
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/16/3/1260/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/16/3/1260/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Shahbaz, Muhammad & Song, Malin & Ahmad, Shabbir & Vo, Xuan Vinh, 2022. "Does economic growth stimulate energy consumption? The role of human capital and R&D expenditures in China," Energy Economics, Elsevier, vol. 105(C).
    2. Peter Kuhn & Mikal Skuterud, 2004. "Internet Job Search and Unemployment Durations," American Economic Review, American Economic Association, vol. 94(1), pages 218-232, March.
    3. Bernard Hoekman & Ben Shepherd, 2017. "Services Productivity, Trade Policy and Manufacturing Exports," The World Economy, Wiley Blackwell, vol. 40(3), pages 499-516, March.
    4. Zhang, Ning & Zhou, P. & Choi, Yongrok, 2013. "Energy efficiency, CO2 emission performance and technology gaps in fossil fuel electricity generation in Korea: A meta-frontier non-radial directional distance functionanalysis," Energy Policy, Elsevier, vol. 56(C), pages 653-662.
    5. Wu, Haitao & Hao, Yu & Ren, Siyu, 2020. "How do environmental regulation and environmental decentralization affect green total factor energy efficiency: Evidence from China," Energy Economics, Elsevier, vol. 91(C).
    6. Peter Dauvergne, 2022. "Is artificial intelligence greening global supply chains? Exposing the political economy of environmental costs," Review of International Political Economy, Taylor & Francis Journals, vol. 29(3), pages 696-718, May.
    7. Lee, Chien-Chiang & Yuan, Zihao & Wang, Qiaoru, 2022. "How does information and communication technology affect energy security? International evidence," Energy Economics, Elsevier, vol. 109(C).
    8. Wang, En-Ze & Lee, Chien-Chiang & Li, Yaya, 2022. "Assessing the impact of industrial robots on manufacturing energy intensity in 38 countries," Energy Economics, Elsevier, vol. 105(C).
    9. Vu, Khuong M., 2011. "ICT as a source of economic growth in the information age: Empirical evidence from the 1996-2005 period," Telecommunications Policy, Elsevier, vol. 35(4), pages 357-372, May.
    10. Wen Chen & Ying Wu, 2022. "Does intellectual property protection stimulate digital economy development?," Journal of Applied Economics, Taylor & Francis Journals, vol. 25(1), pages 723-730, December.
    11. Ren, Siyu & Hao, Yu & Xu, Lu & Wu, Haitao & Ba, Ning, 2021. "Digitalization and energy: How does internet development affect China's energy consumption?," Energy Economics, Elsevier, vol. 98(C).
    12. Yang Liu & Yanlin Yang & Huihui Li & Kaiyang Zhong, 2022. "Digital Economy Development, Industrial Structure Upgrading and Green Total Factor Productivity: Empirical Evidence from China’s Cities," IJERPH, MDPI, vol. 19(4), pages 1-23, February.
    13. Ma, Dan & Zhu, Qing, 2022. "Innovation in emerging economies: Research on the digital economy driving high-quality green development," Journal of Business Research, Elsevier, vol. 145(C), pages 801-813.
    14. Pittman, Russell W, 1983. "Multilateral Productivity Comparisons with Undesirable Outputs," Economic Journal, Royal Economic Society, vol. 93(372), pages 883-891, December.
    15. Fabozzi, Frank J. & Focardi, Sergio & Ponta, Linda & Rivoire, Manon & Mazza, Davide, 2022. "The economic theory of qualitative green growth," Structural Change and Economic Dynamics, Elsevier, vol. 61(C), pages 242-254.
    16. Tone, Kaoru, 2001. "A slacks-based measure of efficiency in data envelopment analysis," European Journal of Operational Research, Elsevier, vol. 130(3), pages 498-509, May.
    17. Lange, Steffen & Pohl, Johanna & Santarius, Tilman, 2020. "Digitalization and energy consumption. Does ICT reduce energy demand?," Ecological Economics, Elsevier, vol. 176(C).
    18. Loures, L. & Ferreira, P., 2019. "Energy consumption as a condition for per capita carbon dioxide emission growth: The results of a qualitative comparative analysis in the European Union," Renewable and Sustainable Energy Reviews, Elsevier, vol. 110(C), pages 220-225.
    19. Yang Shen & Zhihong Yang, 2023. "Chasing Green: The Synergistic Effect of Industrial Intelligence on Pollution Control and Carbon Reduction and Its Mechanisms," Sustainability, MDPI, vol. 15(8), pages 1-22, April.
    20. Jeff Borland & Michael Coelli, 2017. "Are Robots Taking Our Jobs?," Australian Economic Review, The University of Melbourne, Melbourne Institute of Applied Economic and Social Research, vol. 50(4), pages 377-397, December.
    21. Du, Kerui & Li, Pengzhen & Yan, Zheming, 2019. "Do green technology innovations contribute to carbon dioxide emission reduction? Empirical evidence from patent data," Technological Forecasting and Social Change, Elsevier, vol. 146(C), pages 297-303.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Chishti, Muhammad Zubair & Dogan, Eyup & Binsaeed, Rima H., 2024. "Can artificial intelligence and green finance affect economic cycles?," Technological Forecasting and Social Change, Elsevier, vol. 209(C).

    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 & He, Zhi-Wen & Yuan, Zihao, 2023. "A pathway to sustainable development: Digitization and green productivity," Energy Economics, Elsevier, vol. 124(C).
    2. Shunbin Zhong & Huafu Shen & Ziheng Niu & Yang Yu & Lin Pan & Yaojun Fan & Atif Jahanger, 2022. "Moving towards Environmental Sustainability: Can Digital Economy Reduce Environmental Degradation in China?," IJERPH, MDPI, vol. 19(23), pages 1-23, November.
    3. 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).
    4. Wen, Huwei & Liang, Weitao & Lee, Chien-Chiang, 2022. "Urban broadband infrastructure and green total-factor energy efficiency in China," Utilities Policy, Elsevier, vol. 79(C).
    5. Zhang, Weike & Zeng, Ming, 2024. "Is artificial intelligence a curse or a blessing for enterprise energy intensity? Evidence from China," Energy Economics, Elsevier, vol. 134(C).
    6. Li, Yaya & Zhang, Yuru & Pan, An & Han, Minchun & Veglianti, Eleonora, 2022. "Carbon emission reduction effects of industrial robot applications: Heterogeneity characteristics and influencing mechanisms," Technology in Society, Elsevier, vol. 70(C).
    7. Jia, Shanghui & Chen, Xinhui & Jin, Jiayu, 2024. "Digital disruption and energy efficiency: The impact of regional digitalization on China's industrial sector," Energy, Elsevier, vol. 300(C).
    8. Hongdan Xu & Jiuhe Wang, 2024. "Digital Economy, Regional Cooperative Innovation and Green Innovation Efficiency: Game Model and Empirical Evidence Based on Regions in China," Sustainability, MDPI, vol. 16(12), pages 1-18, June.
    9. Junwei Zhao & Yuxiang Zhang & Anhang Chen & Huiqin Zhang, 2022. "Analysis on the Spatio-Temporal Evolution Characteristics of the Impact of China’s Digitalization Process on Green Total Factor Productivity," IJERPH, MDPI, vol. 19(22), pages 1-21, November.
    10. Luyang Tang & Bangke Lu & Tianhai Tian, 2023. "The Effect of Input Digitalization on Carbon Emission Intensity: An Empirical Analysis Based on China’s Manufacturing," IJERPH, MDPI, vol. 20(4), pages 1-22, February.
    11. Du, Juntao & Shen, Zhiyang & Song, Malin & Zhang, Linda, 2023. "Nexus between digital transformation and energy technology innovation: An empirical test of A-share listed enterprises," Energy Economics, Elsevier, vol. 120(C).
    12. Wang, Bo & Wang, Jianda & Dong, Kangyin & Nepal, Rabindra, 2024. "How does artificial intelligence affect high-quality energy development? Achieving a clean energy transition society," Energy Policy, Elsevier, vol. 186(C).
    13. Lee, Chien-Chiang & He, Zhi-Wen & Xiao, Fu, 2022. "How does information and communication technology affect renewable energy technology innovation? International evidence," Renewable Energy, Elsevier, vol. 200(C), pages 546-557.
    14. Dong, Kangyin & Liu, Yang & Wang, Jianda & Dong, Xiucheng, 2024. "Is the digital economy an effective tool for decreasing energy vulnerability? A global case," Ecological Economics, Elsevier, vol. 216(C).
    15. Songqin Zhao & Diyun Peng & Huwei Wen & Huilin Song, 2022. "Does the Digital Economy Promote Upgrading the Industrial Structure of Chinese Cities?," Sustainability, MDPI, vol. 14(16), pages 1-19, August.
    16. Zhiqiang Cai & Wenjie Zhang, 2024. "Quantitative evidence of the community of shared future for mankind as a driver of sustainable development in human society," Palgrave Communications, Palgrave Macmillan, vol. 11(1), pages 1-16, December.
    17. Bai, Ling & Guo, Tianran & Xu, Wei & Liu, Yaobin & Kuang, Ming & Jiang, Lei, 2023. "Effects of digital economy on carbon emission intensity in Chinese cities: A life-cycle theory and the application of non-linear spatial panel smooth transition threshold model," Energy Policy, Elsevier, vol. 183(C).
    18. Kangni Lyu & Shuwang Yang & Kun Zheng & Yao Zhang, 2023. "How Does the Digital Economy Affect Carbon Emission Efficiency? Evidence from Energy Consumption and Industrial Value Chain," Energies, MDPI, vol. 16(2), pages 1-20, January.
    19. Kun Liu & Xuemin Liu & Zihao Wu, 2024. "Nexus between Corporate Digital Transformation and Green Technological Innovation Performance: The Mediating Role of Optimizing Resource Allocation," Sustainability, MDPI, vol. 16(3), pages 1-21, February.
    20. Wen, Huwei & Zhong, Qiming & Lee, Chien-Chiang, 2022. "Digitalization, competition strategy and corporate innovation: Evidence from Chinese manufacturing listed companies," International Review of Financial Analysis, Elsevier, vol. 82(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:gam:jsusta:v:16:y:2024:i:3:p:1260-:d:1331832. 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.