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Intelligence and Green Total Factor Productivity Based on China’s Province-Level Manufacturing Data

Author

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  • Yining Zhang

    (Business School, University of Shanghai for Science and Technology, Shanghai 200093, China)

  • Zhong Wu

    (Business School, University of Shanghai for Science and Technology, Shanghai 200093, China)

Abstract

The application of intelligent technology has an important impact on the green total factor productivity of China’s manufacturing industry. Based on the provincial panel data of China’s manufacturing industry from 2008 to 2017, this article uses the Malmquist–Luenburger (ML) model to measure the green total factor productivity of China’s manufacturing industry, and further constructs an empirical model to analyze the impact mechanism of intelligence on green total factor productivity. The results show that intelligence can increase the green total factor productivity of the manufacturing industry. At the same time, mechanism analysis shows that intelligence can affect manufacturing green total factor productivity by improving technical efficiency. However, the effect of intelligence on the technological progress of the manufacturing industry is not significant. In addition, the impact of intelligence has regional heterogeneity. It has significantly promoted the green total factor productivity in the eastern and central regions of China, while its role in the western region is not obvious. The research in this article confirms that intelligence has a significant positive impact on the green total factor productivity of the manufacturing industry, and can provide suggestion for the current further promotion of the deep integration of intelligence and the green development of the manufacturing industry to achieve the strategic goal of industrial upgrading.

Suggested Citation

  • Yining Zhang & Zhong Wu, 2021. "Intelligence and Green Total Factor Productivity Based on China’s Province-Level Manufacturing Data," Sustainability, MDPI, vol. 13(9), pages 1-16, April.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:9:p:4989-:d:546024
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    11. Wang, Jianda & Wang, Kun & Dong, Kangyin & Zhang, Shiqiu, 2023. "Assessing the role of financial development in natural resource utilization efficiency: Does artificial intelligence technology matter?," Resources Policy, Elsevier, vol. 85(PA).

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