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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
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    1. 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.
    2. 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).
    3. 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.
    4. 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.
    5. Bernard Hoekman & Ben Shepherd, 2017. "Services Productivity, Trade Policy and Manufacturing Exports," The World Economy, Wiley Blackwell, vol. 40(3), pages 499-516, March.
    6. Peter Kuhn & Mikal Skuterud, 2004. "Internet Job Search and Unemployment Durations," American Economic Review, American Economic Association, vol. 94(1), pages 218-232, March.
    7. 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).
    8. 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.
    9. 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.
    10. 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.
    11. 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.
    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. 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.
    14. Pittman, Russell W, 1983. "Multilateral Productivity Comparisons with Undesirable Outputs," Economic Journal, Royal Economic Society, vol. 93(372), pages 883-891, December.
    15. 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.
    16. Lange, Steffen & Pohl, Johanna & Santarius, Tilman, 2020. "Digitalization and energy consumption. Does ICT reduce energy demand?," Ecological Economics, Elsevier, vol. 176(C).
    17. 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.
    18. 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.
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