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Impact of Industrial Intelligence on China’s Urban Land Green Utilization Efficiency

Author

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  • Chunyan Zhao

    (School of Economics and Finance, Xi’an Jiaotong University, Xian 710061, China)

  • Linjing Wang

    (School of Economics and Finance, Xi’an Jiaotong University, Xian 710061, China)

  • Chaobo Zhou

    (Climate Change and Energy Economics Study Center, Economics and Management School, Wuhan University, Wuhan 430072, China)

Abstract

Against the backdrop of the fourth technological revolution, industrial intelligence (INDI) represented by industrial robots has rapidly developed. This evolution provides favorable opportunities for precise decision-making in pollution control and achieving China’s “dual carbon” goals. Previous studies have mainly discussed the economic effects of INDI from the perspective of the labor market. This study shifts its focus to examining the impact of INDI on the land green utilization efficiency (LGUE) in cities. Using the panel data of Chinese cities spanning 2009–2021, this study empirically tests the effect and transmission mechanism of INDI on LGUE. We find that urban INDI significantly enhances LGUE. In terms of its transmission mechanism, INDI drives improvements in urban LGUE through technological progress, energy structure optimization, and industrial structure upgrading. Urban infrastructure construction and financial agglomeration level can further strengthen the positive impact of INDI on LGUE. In addition, the improvement in LGUE due to INDI is more significant in non-resource-based and large-sized cities than resource-based and small and medium-sized cities. Therefore, each region should enhance the integration of intelligent technology with traditional industrial manufacturing. Doing so is essential to establish comprehensive assessment indicators that balance environmental protection and economic growth, strengthen regional information infrastructure construction, ensure steady financial flow, and support green development initiatives across regions.

Suggested Citation

  • Chunyan Zhao & Linjing Wang & Chaobo Zhou, 2024. "Impact of Industrial Intelligence on China’s Urban Land Green Utilization Efficiency," Land, MDPI, vol. 13(8), pages 1-17, August.
    • Handle: RePEc:gam:jlands:v:13:y:2024:i:8:p:1312-:d:1459254
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    References listed on IDEAS

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    1. Grossman, G.M & Krueger, A.B., 1991. "Environmental Impacts of a North American Free Trade Agreement," Papers 158, Princeton, Woodrow Wilson School - Public and International Affairs.
    2. Daron Acemoglu & Pascual Restrepo, 2019. "Automation and New Tasks: How Technology Displaces and Reinstates Labor," Journal of Economic Perspectives, American Economic Association, vol. 33(2), pages 3-30, Spring.
    3. 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.
    4. Lan, Fei & Sun, Li & Pu, Wenyan, 2021. "Research on the influence of manufacturing agglomeration modes on regional carbon emission and spatial effect in China," Economic Modelling, Elsevier, vol. 96(C), pages 346-352.
    5. Imbens, Guido W & Angrist, Joshua D, 1994. "Identification and Estimation of Local Average Treatment Effects," Econometrica, Econometric Society, vol. 62(2), pages 467-475, March.
    6. Timothy D. Searchinger & Stefan Wirsenius & Tim Beringer & Patrice Dumas, 2018. "Assessing the efficiency of changes in land use for mitigating climate change," Nature, Nature, vol. 564(7735), pages 249-253, December.
    7. Yang, Siying & Liu, Fengshuo, 2024. "Impact of industrial intelligence on green total factor productivity: The indispensability of the environmental system," Ecological Economics, Elsevier, vol. 216(C).
    Full references (including those not matched with items on IDEAS)

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