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Sustainable manufacturing intelligence: pathways for high-quality and energy efficient economic growth

Author

Listed:
  • Lei Xia

    (Wuhan Polytechnic University)

  • Qingjiang Han

    (Jiangxi University of Finance and Economics)

  • Shui Yu

    (Wuhan Polytechnic University)

Abstract

Manufacturing intelligence is an important starting point to achieve high-quality economic growth. Based on the non-radial and non-angular DEA-SBM model, combined with the Malmquist index method, this paper calculates the total factor productivity and green total factor productivity of 286 prefecture-level cities in China, which are used as the indicators of high-quality economic growth. At the same time, the proxy variables of manufacturing intelligence are further constructed, and the impact of intelligent manufacturing on high-quality and energy-efficient economic growth is empirically analyzed. It is found that intelligent manufacturing significantly promotes China’s high-quality and energy-efficient economic growth. Productivity is further decomposed into specific indicators such as technical efficiency, technological progress, pure technical efficiency, pure technical progress, scale efficiency and scale technology, and the mechanism of intelligent manufacturing is analyzed from multiple angles. The research finds that manufacturing intelligence improves the total factor productivity through technological progress effect. Green total factor productivity has been improved through the improvement of technical efficiency effect, but the development of intelligence has brought new challenges to China's labor market. In order to further develop the potential of intelligence, it is necessary to further improve the enterprises’ scale efficiency, while increasing the research and development of energy technology.

Suggested Citation

  • Lei Xia & Qingjiang Han & Shui Yu, 2024. "Sustainable manufacturing intelligence: pathways for high-quality and energy efficient economic growth," Economic Change and Restructuring, Springer, vol. 57(3), pages 1-30, June.
    • Handle: RePEc:kap:ecopln:v:57:y:2024:i:3:d:10.1007_s10644-024-09692-z
    DOI: 10.1007/s10644-024-09692-z
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