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Does AI Application Matter in Promoting Carbon Productivity? Fresh Evidence from 30 Provinces in China

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  • Shan Feng

    (School of Economics, Ocean University of China, Qingdao 266100, China)

  • Shuguang Liu

    (School of Economics, Ocean University of China, Qingdao 266100, China
    Institute of Ocean Development, Key Research Base of Humanities and Social Sciences, Ministry of Education, Qingdao 266100, China)

Abstract

Artificial intelligence (AI) is an important force leading to a new round of scientific and technological revolution, as well as promoting the realization of the dual carbon goals of China. Determining how to take advantage of AI during the green industrial transformation and propelling participation in global value chains are of great importance to China. In this paper, we carefully study the influencing mechanism. The Batik Variable Method is then applied to measure robot penetration in the industries across 30 provinces in China from 2010 to 2019. Furthermore, intermediate and threshold effect models are constructed using three crucial variables. The estimates reveal critical findings: firstly, the application of AI has a significant positive effect on carbon productivity, and this conclusion is still valid after a series of robustness tests. Secondly, a heterogeneity test shows that, compared with the central and western regions, AI application in the east has a stronger and more significant effect on carbon productivity over time. Thirdly, the optimization of human capital and improvement of innovation level both play partial mediating roles in this process, and manufacturing agglomeration has a nonlinear adjustment effect on the positive relationship between AI application and carbon productivity. The conclusions of this study provide references for further optimizing and expanding the application scenarios of AI, thereby contributing to high-quality economic development in China.

Suggested Citation

  • Shan Feng & Shuguang Liu, 2023. "Does AI Application Matter in Promoting Carbon Productivity? Fresh Evidence from 30 Provinces in China," Sustainability, MDPI, vol. 15(23), pages 1-19, November.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:23:p:16261-:d:1286804
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    References listed on IDEAS

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    1. Hansen, Bruce E., 1999. "Threshold effects in non-dynamic panels: Estimation, testing, and inference," Journal of Econometrics, Elsevier, vol. 93(2), pages 345-368, December.
    2. Chang, Lei & Taghizadeh-Hesary, Farhad & Mohsin, Muhammad, 2023. "Role of artificial intelligence on green economic development: Joint determinates of natural resources and green total factor productivity," Resources Policy, Elsevier, vol. 82(C).
    3. Krugman, Paul, 1991. "Increasing Returns and Economic Geography," Journal of Political Economy, University of Chicago Press, vol. 99(3), pages 483-499, June.
    4. Senhua Huang & Wenzhong Ye & Feng Han, 2023. "Does the Digital Economy Promote Industrial Collaboration and Agglomeration? Evidence from 286 Cities in China," Sustainability, MDPI, vol. 15(19), pages 1-24, October.
    5. Daron Acemoglu & Pascual Restrepo, 2018. "Artificial Intelligence, Automation, and Work," NBER Chapters, in: The Economics of Artificial Intelligence: An Agenda, pages 197-236, National Bureau of Economic Research, Inc.
    6. Xianpu Xu & Yuchen Song, 2023. "Is There a Conflict between Automation and Environment? Implications of Artificial Intelligence for Carbon Emissions in China," Sustainability, MDPI, vol. 15(16), pages 1-22, August.
    7. Steven Chu & Arun Majumdar, 2012. "Opportunities and challenges for a sustainable energy future," Nature, Nature, vol. 488(7411), pages 294-303, August.
    8. Daron Acemoglu & Pascual Restrepo, 2020. "Robots and Jobs: Evidence from US Labor Markets," Journal of Political Economy, University of Chicago Press, vol. 128(6), pages 2188-2244.
    9. Zhenyu Jiang & Zongjun Wang & Yanqi Zeng, 2020. "Can voluntary environmental regulation promote corporate technological innovation?," Business Strategy and the Environment, Wiley Blackwell, vol. 29(2), pages 390-406, February.
    10. Can Li & Qi He & Han Ji & Shengguo Yu & Jiao Wang, 2023. "Reexamining the Impact of Global Value Chain Participation on Regional Economic Growth: New Evidence Based on a Nonlinear Model and Spatial Spillover Effects with Panel Data from Chinese Cities," Sustainability, MDPI, vol. 15(18), pages 1-31, September.
    11. Farhad Taghizadeh-Hesary & Naoyuki Yoshino & Muhammad Mohsin & Nawazish Mirza, 2022. "Introduction: Ways To Achieve Green Economic Recovery And Mitigate Greenhouse Gases In The Post-Covid-19 Era," Climate Change Economics (CCE), World Scientific Publishing Co. Pte. Ltd., vol. 13(03), pages 1-3, August.
    12. Mo Chen & Xuhua Hu & Jijian Zhang & Zhe Xu & Guang Yang & Zenan Sun, 2023. "Are Firms More Willing to Seek Green Technology Innovation in the Context of Economic Policy Uncertainty? —Evidence from China," Sustainability, MDPI, vol. 15(19), pages 1-24, September.
    13. Daron Acemoglu & Pascual Restrepo, 2018. "Artificial Intelligence, Automation and Work," Boston University - Department of Economics - Working Papers Series dp-298, Boston University - Department of Economics.
    14. Ping Chen & Jiawei Gao & Zheng Ji & Han Liang & Yu Peng, 2022. "Do Artificial Intelligence Applications Affect Carbon Emission Performance?—Evidence from Panel Data Analysis of Chinese Cities," Energies, MDPI, vol. 15(15), pages 1-16, August.
    15. Hao Yao & Xiulin Gu & Qing Yu, 2023. "Impact of Graduate Student Expansion and Innovative Human Capital on Green Total Factor Productivity," Sustainability, MDPI, vol. 15(2), pages 1-15, January.
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