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Has the Digital Economy Improved the Urban Land Green Use Efficiency? Evidence from the National Big Data Comprehensive Pilot Zone Policy

Author

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  • Guangya Zhou

    (School of Economics and Trade, Hunan University, Changsha 410006, China)

  • Helian Xu

    (School of Economics and Trade, Hunan University, Changsha 410006, China)

  • Chuanzeng Jiang

    (School of Finance and Statistics, Hunan University, Changsha 410006, China)

  • Shiqi Deng

    (School of Economics and Trade, Hunan University, Changsha 410006, China)

  • Liming Chen

    (School of Finance and Statistics, Hunan University, Changsha 410006, China)

  • Zhi Zhang

    (School of Economics and Trade, Hunan University, Changsha 410006, China)

Abstract

The advancement of the big data industry is playing a pivotal role in urban land management refinement. Recently, China initiated a big data strategy, establishing national big data comprehensive pilot zones (NBDCPZs) across diverse regions. These initiatives present substantial opportunities for enhancing the urban land green use efficiency (ULGUE). Consequently, in this study, we utilized the super-efficiency slack-based measure (SBM) model with undesirable outputs to assess the ULGUEs across 281 prefecture-level cities in China from 2006 to 2021. Subsequently, leveraging the NBDCPZ establishment as a quasi-natural experiment, we employed the difference-in-differences (DID) method to empirically explore the impact of the NBDCPZ policy on the ULGUE for the first time. The findings revealed the following: (1) The implementation of the NBDCPZ policy significantly enhances the ULGUE; (2) the effects are mediated through mechanisms such as fostering technological innovation, mitigating resource misallocation, and promoting industrial agglomeration; (3) the heterogeneity analysis emphasizes the increased policy effectiveness in cities characterized by fewer natural resources, lower economic growth pressures, stable development stages, and moderate digital infrastructure and human capital levels; and (4) further analysis demonstrates the significant positive spillover effects of the NBDCPZ policy on the ULGUEs of neighboring non-pilot cities, with a diminishing impact as the proximity between pilot and non-pilot cities decreases. Overall, this study contributes to the literature on the relationship between the digital economy and land utilization, offering valuable insights for achieving sustainable urban development.

Suggested Citation

  • Guangya Zhou & Helian Xu & Chuanzeng Jiang & Shiqi Deng & Liming Chen & Zhi Zhang, 2024. "Has the Digital Economy Improved the Urban Land Green Use Efficiency? Evidence from the National Big Data Comprehensive Pilot Zone Policy," Land, MDPI, vol. 13(7), pages 1-25, June.
    • Handle: RePEc:gam:jlands:v:13:y:2024:i:7:p:960-:d:1426104
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    as
    1. 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.
    2. Gianni Guastella & Stefano Pareglio & Paolo Sckokai, 2017. "A Spatial Econometric Analysis of Land Use Efficiency in Large and Small Municipalities," Working Papers 2017.03, Fondazione Eni Enrico Mattei.
    3. Hamilton, R.H. & Sodeman, William A., 2020. "The questions we ask: Opportunities and challenges for using big data analytics to strategically manage human capital resources," Business Horizons, Elsevier, vol. 63(1), pages 85-95.
    4. Chen, Chuanglian & Wang, Shudan & Yao, Shujie & Lin, Yuting, 2023. "Does digital transformation increase the labor income share? From a perspective of resources reallocation," Economic Modelling, Elsevier, vol. 128(C).
    5. Wang, Wei & Zhang, Hongguang & Sun, Ziyuan & Wang, Lihong & Zhao, Jianying & Wu, Fengzhi, 2023. "Can digital policy improve corporate sustainability? Empirical evidence from China's national comprehensive big data pilot zones," Telecommunications Policy, Elsevier, vol. 47(9).
    6. Diego Restuccia & Richard Rogerson, 2017. "The Causes and Costs of Misallocation," Journal of Economic Perspectives, American Economic Association, vol. 31(3), pages 151-174, Summer.
    7. Krugman, Paul, 1991. "Increasing Returns and Economic Geography," Journal of Political Economy, University of Chicago Press, vol. 99(3), pages 483-499, June.
    8. Chagas, André L.S. & Azzoni, Carlos R. & Almeida, Alexandre N., 2016. "A spatial difference-in-differences analysis of the impact of sugarcane production on respiratory diseases," Regional Science and Urban Economics, Elsevier, vol. 59(C), pages 24-36.
    9. GONG, Xingyu & YU, Jintao & LUO, Xi, 2024. "Will the establishment of city commercial banks improve resource allocation efficiency?–Concurrently on bank risk-taking channels of monetary policy in China," Economic Analysis and Policy, Elsevier, vol. 81(C), pages 1513-1526.
    10. Chang-Tai Hsieh & Peter J. Klenow, 2009. "Misallocation and Manufacturing TFP in China and India," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 124(4), pages 1403-1448.
    11. Pan, Minjie & Zhao, Xin & lv, Kangjuan & Rosak-Szyrocka, Joanna & Mentel, Grzegorz & Truskolaski, Tadeusz, 2023. "Internet development and carbon emission-reduction in the era of digitalization: Where will resource-based cities go?," Resources Policy, Elsevier, vol. 81(C).
    12. Xie, Hualin & Chen, Qianru & Wang, Wei & He, Yafen, 2018. "Analyzing the green efficiency of arable land use in China," Technological Forecasting and Social Change, Elsevier, vol. 133(C), pages 15-28.
    13. Zhao, Zhe & Bai, Yuping & Wang, Guofeng & Chen, Jiancheng & Yu, Jiangli & Liu, Wei, 2018. "Land eco-efficiency for new-type urbanization in the Beijing-Tianjin-Hebei Region," Technological Forecasting and Social Change, Elsevier, vol. 137(C), pages 19-26.
    14. Gao, Qiang & Cheng, Changming & Sun, Guanglin, 2023. "Big data application, factor allocation, and green innovation in Chinese manufacturing enterprises," Technological Forecasting and Social Change, Elsevier, vol. 192(C).
    15. Baier, Scott L. & Bergstrand, Jeffrey H., 2007. "Do free trade agreements actually increase members' international trade?," Journal of International Economics, Elsevier, vol. 71(1), pages 72-95, March.
    16. Bai, Caiquan & Xie, Dongshui & Zhang, Yuwei, 2024. "Industrial land transfer and enterprise pollution emissions: Evidence from China," Economic Analysis and Policy, Elsevier, vol. 81(C), pages 181-194.
    17. Gao, Ya & Li, Bing & Sun, Weizeng, 2023. "Resource curse on innovation: A perspective on local mining industry monopolies in resource-based cities in China," China Economic Review, Elsevier, vol. 81(C).
    18. 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.
    19. Yuan Feng & Ying Li & Changfei Nie, 2023. "The Effect of Place-Based Policy on Urban Land Green Use Efficiency: Evidence from the Pilot Free-Trade Zone Establishment in China," Land, MDPI, vol. 12(3), pages 1-19, March.
    20. Li, Zhenghui & Huang, Zimei & Su, Yaya, 2023. "New media environment, environmental regulation and corporate green technology innovation:Evidence from China," Energy Economics, Elsevier, vol. 119(C).
    21. Lyu, Yanwei & Xiao, Xuan & Zhang, Jinning, 2024. "Does the digital economy enhance green total factor productivity in China? The evidence from a national big data comprehensive pilot zone," Structural Change and Economic Dynamics, Elsevier, vol. 69(C), pages 183-196.
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    Cited by:

    1. Xiang Peng & Deheng Xiao, 2024. "Can Open Government Data Improve City Green Land-Use Efficiency? Evidence from China," Land, MDPI, vol. 13(11), pages 1-17, November.

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