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The Impact of Digital Economy Agglomeration on Regional Green Total Factor Productivity Disparity: Evidence from 285 Cities in China

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  • Kai Chen

    (School of Business Administration, Northeastern University, Shenyang 110169, China
    School of Economics, Northeastern University at Qinhuangdao, Qinhuangdao 066004, China)

  • Feng Guo

    (School of Business Administration, Northeastern University, Shenyang 110169, China)

  • Shuang Xu

    (School of Business Administration, Northeastern University, Shenyang 110169, China)

Abstract

Purpose—The unbalanced layout of digital economy agglomeration has a significant impact on regional high-quality development. This study aims to explore the impact of digital economy agglomeration on regional green total factor productivity (GTFP) disparity from two aspects, including theoretical mechanism and empirical effect. Design/methodology/approach—Based on the empirical data of 285 cities above the prefecture level in China from 2003 to 2018, super-efficiency undesired SBM model, spatial Dubin model, and intermediary effect model are utilized to analyze how digital economy agglomeration affects regional GTFP disparity. Findings—The results show that the GTFP of China is on the rise as a whole, but the gap among cities is gradually expanding. Digital economy agglomeration has significant positive direct effects and positive spillover effects on GTFP, but digital economy agglomeration also aggravates the regional GTFP disparity due to disequilibrium industrial upgrading mechanism. Originality/value—The paper confirms the relationship between digital economy agglomeration and regional GTFP disparity for the first time. Different from previous studies, the industrial upgrading mechanism in this paper includes industrial structure upgrading and industrial spatial evolution. The study calls for the industrial bottleneck of “low-end locking” in underdeveloped cities to be noticed.

Suggested Citation

  • Kai Chen & Feng Guo & Shuang Xu, 2022. "The Impact of Digital Economy Agglomeration on Regional Green Total Factor Productivity Disparity: Evidence from 285 Cities in China," Sustainability, MDPI, vol. 14(22), pages 1-16, November.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:22:p:14676-:d:966181
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    1. Fujita , Masahisa & Krugman, Paul, 2004. "The new economic geography: Past, present and the future," INVESTIGACIONES REGIONALES - Journal of REGIONAL RESEARCH, Asociación Española de Ciencia Regional, issue 4, pages 177-206.
    2. Shuming Ren & Lianqing Li & Yueqi Han & Yu Hao & Haitao Wu, 2022. "The emerging driving force of inclusive green growth: Does digital economy agglomeration work?," Business Strategy and the Environment, Wiley Blackwell, vol. 31(4), pages 1656-1678, May.
    3. Ciccone, Antonio & Hall, Robert E, 1996. "Productivity and the Density of Economic Activity," American Economic Review, American Economic Association, vol. 86(1), pages 54-70, March.
    4. Pierre‐Philippe Combes & Gilles Duranton & Laurent Gobillon & Diego Puga & Sébastien Roux, 2012. "The Productivity Advantages of Large Cities: Distinguishing Agglomeration From Firm Selection," Econometrica, Econometric Society, vol. 80(6), pages 2543-2594, November.
    5. Qiang Liu & Shengxia Xu & Xiaoli Lu, 2020. "Imbalance measurement of regional economic quality development: evidence from China," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 65(2), pages 527-556, October.
    6. repec:hal:pseose:hal-00812695 is not listed on IDEAS
    7. Julio Martínez-Galarraga & Elisenda Paluzie & Jordi Pons & Daniel A. Tirado-Fabregat, 2008. "Agglomeration and labour productivity in Spain over the long term," Cliometrica, Journal of Historical Economics and Econometric History, Association Française de Cliométrie (AFC), vol. 2(3), pages 195-212, October.
    8. Tone, Kaoru, 2002. "A slacks-based measure of super-efficiency in data envelopment analysis," European Journal of Operational Research, Elsevier, vol. 143(1), pages 32-41, November.
    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. Junfeng Zhang & Hong Fang & Bo Peng & Xu Wang & Siran Fang, 2016. "Productivity Growth-Accounting for Undesirable Outputs and Its Influencing Factors: The Case of China," Sustainability, MDPI, vol. 8(11), pages 1-13, November.
    11. Zhang, Shengling & Wang, Yao & Hao, Yu & Liu, Zhiwei, 2021. "Shooting two hawks with one arrow: Could China's emission trading scheme promote green development efficiency and regional carbon equality?," Energy Economics, Elsevier, vol. 101(C).
    12. Richard E. Baldwin & Toshihiro Okubo, 2006. "Heterogeneous firms, agglomeration and economic geography: spatial selection and sorting," Journal of Economic Geography, Oxford University Press, vol. 6(3), pages 323-346, June.
    13. Fu, Yuming & Gabriel, Stuart A., 2012. "Labor migration, human capital agglomeration and regional development in China," Regional Science and Urban Economics, Elsevier, vol. 42(3), pages 473-484.
    14. Jing Han & Xi Chen & Yawen Sun, 2021. "Technology or Institutions: Which Is the Source of Green Economic Growth in Chinese Cities?," Sustainability, MDPI, vol. 13(19), pages 1-20, October.
    15. Jinqi Su & Ke Su & Shubin Wang, 2021. "Does the Digital Economy Promote Industrial Structural Upgrading?—A Test of Mediating Effects Based on Heterogeneous Technological Innovation," Sustainability, MDPI, vol. 13(18), pages 1-24, September.
    16. Jinhua Sun & Decai Tang & Haojia Kong & Valentina Boamah, 2022. "Impact of Industrial Structure Upgrading on Green Total Factor Productivity in the Yangtze River Economic Belt," IJERPH, MDPI, vol. 19(6), pages 1-15, March.
    17. W. Edward STEINMUELLER, 2001. "ICTs and the possibilities for leapfrogging by developing countries," International Labour Review, International Labour Organization, vol. 140(2), pages 193-210, June.
    18. Yuanxin Peng & Zhuo Chen & Juanzhi Xu & Jay Lee, 2020. "Analysis of green total factor productivity trend and its determinants for the countries along silk roads," Growth and Change, Wiley Blackwell, vol. 51(4), pages 1711-1726, December.
    19. Yang Liu & Yanlin Yang & Huihui Li & Kaiyang Zhong, 2022. "Digital Economy Development, Industrial Structure Upgrading and Green Total Factor Productivity: Empirical Evidence from China’s Cities," IJERPH, MDPI, vol. 19(4), pages 1-23, February.
    20. Ma, Dan & Zhu, Qing, 2022. "Innovation in emerging economies: Research on the digital economy driving high-quality green development," Journal of Business Research, Elsevier, vol. 145(C), pages 801-813.
    21. Xie, Rui & Fu, Wei & Yao, Siling & Zhang, Qi, 2021. "Effects of financial agglomeration on green total factor productivity in Chinese cities: Insights from an empirical spatial Durbin model," Energy Economics, Elsevier, vol. 101(C).
    22. Nancy Stokey, 2015. "Catching up and falling behind," Journal of Economic Growth, Springer, vol. 20(1), pages 1-36, March.
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    Cited by:

    1. Wang, Jianda & Guo, Dongsheng, 2023. "Siphon and radiation effects of ICT agglomeration on green total factor productivity: Evidence from a spatial Durbin model," Energy Economics, Elsevier, vol. 126(C).
    2. Xiaowen Wang & Nishang Tian & Shuting Wang, 2022. "The Impact of Information and Communication Technology Industrial Co-Agglomeration on Carbon Productivity with the Background of the Digital Economy: Empirical Evidence from China," IJERPH, MDPI, vol. 20(1), pages 1-21, December.

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