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The Impact of Manufacturing Intelligence on Green Development Efficiency: A Study Based on Chinese Data

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  • Xiaozhong Li

    (School of Economics, Hangzhou Dianzi University, Hangzhou 310018, China)

  • Jun Ling

    (School of Economics, Hangzhou Dianzi University, Hangzhou 310018, China)

Abstract

This study used provincial panel data from 2011 to 2020 to empirically analyze the impact of improvements in the manufacturing intelligence level on the efficiency of green development. The results show the following: improvements in manufacturing intelligence significantly increased the efficiency of green development. Following the “Guidelines for the Construction of the National Intelligent Manufacturing Standard System” proposed by China as a quasi-natural experiment, the double-difference method was used to prove the following: the promotional effect is greater in eastern regions after the policy was implemented. An improvement in manufacturing intelligence increases green development efficiency by improving the efficiency of technological innovation and energy use. The impact of the manufacturing intelligence level on green development efficiency shows a threshold effect. The promotion effect is more pronounced in regions where the level of technological innovation and the strength of the government’s role cross this threshold. Therefore, the government should vigorously promote the intelligent transformation of the manufacturing industry and improve the efficiency of China’s green development.

Suggested Citation

  • Xiaozhong Li & Jun Ling, 2023. "The Impact of Manufacturing Intelligence on Green Development Efficiency: A Study Based on Chinese Data," Sustainability, MDPI, vol. 15(9), pages 1-19, May.
  • Handle: RePEc:gam:jsusta:v:15:y:2023:i:9:p:7553-:d:1139473
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    References listed on IDEAS

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    1. Georg Graetz & Guy Michaels, 2018. "Robots at Work," The Review of Economics and Statistics, MIT Press, vol. 100(5), pages 753-768, December.
    2. Toorajipour, Reza & Sohrabpour, Vahid & Nazarpour, Ali & Oghazi, Pejvak & Fischl, Maria, 2021. "Artificial intelligence in supply chain management: A systematic literature review," Journal of Business Research, Elsevier, vol. 122(C), pages 502-517.
    3. Lara Waltersmann & Steffen Kiemel & Julian Stuhlsatz & Alexander Sauer & Robert Miehe, 2021. "Artificial Intelligence Applications for Increasing Resource Efficiency in Manufacturing Companies—A Comprehensive Review," Sustainability, MDPI, vol. 13(12), pages 1-26, June.
    4. Daron Acemoglu & Pascual Restrepo, 2018. "The Race between Man and Machine: Implications of Technology for Growth, Factor Shares, and Employment," American Economic Review, American Economic Association, vol. 108(6), pages 1488-1542, June.
    5. 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).
    6. Yang, Haochang & Li, Lianshui & Liu, Yaobin, 2022. "The effect of manufacturing intelligence on green innovation performance in China," Technological Forecasting and Social Change, Elsevier, vol. 178(C).
    7. Lv, Chengchao & Shao, Changhua & Lee, Chien-Chiang, 2021. "Green technology innovation and financial development: Do environmental regulation and innovation output matter?," Energy Economics, Elsevier, vol. 98(C).
    8. Erik Brynjolfsson & Lorin M. Hitt, 2000. "Beyond Computation: Information Technology, Organizational Transformation and Business Performance," Journal of Economic Perspectives, American Economic Association, vol. 14(4), pages 23-48, Fall.
    9. Cheng, Zhonghua & Li, Lianshui & Liu, Jun, 2020. "Natural resource abundance, resource industry dependence and economic green growth in China," Resources Policy, Elsevier, vol. 68(C).
    10. 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.
    11. Chen, Yang & Cheng, Liang & Lee, Chien-Chiang, 2022. "How does the use of industrial robots affect the ecological footprint? International evidence," Ecological Economics, Elsevier, vol. 198(C).
    Full references (including those not matched with items on IDEAS)

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