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Research on industrial structure adjustment and spillover effect in resource-based regions in the post-pandemic era

Author

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  • Ziqiong He
  • Rongguang Zhang
  • Qiwen Qiu
  • Zhe Chen

Abstract

Resource-based regions support national economic development and are essential sources of basic energy and raw materials. In the post-pandemic era, however, there are practical situations to deal with, such as a fractured industrial chain, a weaker industrial structure, and a sharp reduction in economic benefits. Based on data collected from 68 cities in China, from 2010 to 2021, with 816 observations, this paper explores the industrial development process of resource-based regions in China and the change in the toughness of the industrial structure under the impact of COVID-19. The paper studies and analyzes industrial development trends, industrial structure toughness, and spatial spillover effects. The methods used are the Markov chain model and the Industrial Structure Advancement Index. By building the spatial Dubin model, the paper analyzes the spatial spillover effect of regional industrial development. It decomposes the spillover effect using the partial differential model based on regression. The results show that, during the study period, the comprehensive development level of industries in resource-based regions in China was slowly improving and tended to stabilize after entering the post-pandemic era. The evolution of an advanced industrial structure is significantly heterogeneous among regions, and each region has different toughness. The impact of COVID-19 has reduced the toughness of China’s resource-based regions’ industrial structure. The spatial spillover effect of regional industrial development is significant. Labor force, technology input, and industrial-structure optimization have different impacts on the industrial development of neighboring regions. In the post-pandemic era, China has used new management methods for more innovation. In order to achieve low-carbon, environmental protection, and sustainable development of resources, realize the rapid recovery of the toughness of industrial structure in China’s resource-based cities, and reduce the impact of the COVID-19 pandemic, China proposes to expand the supply of resources, improve the allocation of resources, optimize the direction, promote the rational flow and efficient aggregation of various factors, and enhance the impetus for innovation and development.

Suggested Citation

  • Ziqiong He & Rongguang Zhang & Qiwen Qiu & Zhe Chen, 2024. "Research on industrial structure adjustment and spillover effect in resource-based regions in the post-pandemic era," PLOS ONE, Public Library of Science, vol. 19(1), pages 1-19, January.
  • Handle: RePEc:plo:pone00:0296772
    DOI: 10.1371/journal.pone.0296772
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    References listed on IDEAS

    as
    1. Sun, Yajie & Liao, Wen-Chi, 2021. "Resource-Exhausted City Transition to continue industrial development," China Economic Review, Elsevier, vol. 67(C).
    2. Bevilacqua, Mattia & Duygun, Meryem & Vioto, Davide, 2023. "The impact of COVID-19 related policy interventions on international systemic risk," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 89(C).
    3. Su, Yi & Fan, Qi-ming, 2022. "Renewable energy technology innovation, industrial structure upgrading and green development from the perspective of China's provinces," Technological Forecasting and Social Change, Elsevier, vol. 180(C).
    4. Jia, Zhijie & Wen, Shiyan & Lin, Boqiang, 2021. "The effects and reacts of COVID-19 pandemic and international oil price on energy, economy, and environment in China," Applied Energy, Elsevier, vol. 302(C).
    5. De Long, J Bradford & Andrei Shleifer & Lawrence H. Summers & Robert J. Waldmann, 1990. "Noise Trader Risk in Financial Markets," Journal of Political Economy, University of Chicago Press, vol. 98(4), pages 703-738, August.
    6. Jiang, Fei & Kong, Dongmin & Lu, Zhengfei & Ma, Yongqiang & Yi, Yang, 2023. "Geographic dispersion and corporate resilience during the COVID-19 pandemic," International Review of Financial Analysis, Elsevier, vol. 88(C).
    7. Zheng, Panpan & Li, Zhen & Zhuang, Ziyin, 2024. "The impact of COVID-19 on corporate digital innovation in China: A study based on the DDD model," Finance Research Letters, Elsevier, vol. 59(C).
    8. Shen, Yuchen & Ren, Xiaoping, 2023. "Digital finance and upgrading of industrial structure: Prefecture-level evidence from China," Finance Research Letters, Elsevier, vol. 55(PB).
    9. Li, Qiangyi & Zeng, Fu'e & Liu, Shaohui & Yang, Mian & Xu, Fei, 2021. "The effects of China's sustainable development policy for resource-based cities on local industrial transformation," Resources Policy, Elsevier, vol. 71(C).
    10. Shi, Rubiao & Gao, Pengfei & Su, Xufeng & Zhang, Xi & Yang, Xiaodong, 2024. "Synergizing natural resources and sustainable development: A study of industrial structure, and green innovation in Chinese region," Resources Policy, Elsevier, vol. 88(C).
    11. Jiang, Wei & Dong, Lingfei & Liu, Xinyi, 2023. "How does COVID-19 affect the spillover effects of green finance, carbon markets, and renewable/non-renewable energy markets? Evidence from China," Energy, Elsevier, vol. 281(C).
    12. Rijpma, Auke & van Dijk, Ingrid K. & Schalk, Ruben & Zijdeman, Richard L. & Mourits, Rick J., 2022. "Unequal excess mortality during the Spanish Flu pandemic in the Netherlands," Economics & Human Biology, Elsevier, vol. 47(C).
    13. Zhang, Shikun & Anser, Muhammad Khalid & Yao-Ping Peng, Michael & Chen, Chunchun, 2023. "Visualizing the sustainable development goals and natural resource utilization for green economic recovery after COVID-19 pandemic," Resources Policy, Elsevier, vol. 80(C).
    14. Coutiño, Alfredo & Zandi, Mark, 2021. "Global loss of production capacity caused by the COVID-19 pandemic," Journal of Policy Modeling, Elsevier, vol. 43(3), pages 493-502.
    15. Teng, Yuqiang & Lin, Boqiang, 2024. "The energy-saving effect of industrial chain synergistic division: Evidence from China's industrial chain," Energy Policy, Elsevier, vol. 185(C).
    16. Dong, Qianyu & Zhong, Kaiyi & Liao, Yijia & Xiong, Runli & Wang, Fengbo & Pang, Min, 2023. "Coupling coordination degree of environment, energy, and economic growth in resource-based provinces of China," Resources Policy, Elsevier, vol. 81(C).
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