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Spatial and Temporal Variation Characteristics and Driving Mechanisms of Multidimensional Socio-Economic Development Levels in Resource-Based Cities

Author

Listed:
  • Yiting Su

    (College of Geoscience and Surveying Engineering, China University of Mining and Technology-Beijing, Beijing 100083, China)

  • Jing Li

    (College of Geoscience and Surveying Engineering, China University of Mining and Technology-Beijing, Beijing 100083, China)

  • Shouqiang Yin

    (College of Geoscience and Surveying Engineering, China University of Mining and Technology-Beijing, Beijing 100083, China)

  • Jiabao Yue

    (College of Resource Environment and Tourism, Capital Normal University, Beijing 100048, China)

  • Zhai Jiang

    (College of Geoscience and Surveying Engineering, China University of Mining and Technology-Beijing, Beijing 100083, China)

  • Tianyue Ma

    (College of Geoscience and Surveying Engineering, China University of Mining and Technology-Beijing, Beijing 100083, China)

  • Zhangqian Han

    (College of Geoscience and Surveying Engineering, China University of Mining and Technology-Beijing, Beijing 100083, China)

Abstract

As resources are depleted, resource-based cities face unique challenges in the process of socio-economic development. We constructed a multidimensional socio-economic development level model by adopting Entropy Value Method, Analytical Hierarchy Process, time series weighting method, and Game Theory approach for the data of 10 indicators in 4 dimensions of 115 resource-based cities in China from 2004 to 2019 to explore the spatial and temporal divergence characteristics of multidimensional socio-economic development level and the driving mechanism of its pattern of evolution. The results show that: (1) the overall socio-economic development level of resource-based cities has improved from 2004 to 2019, but the overall level is low. Large differences exist in the spatial distribution of socio-economic development levels between cities with more significant regional spatial aggregation characteristics. (2) Secondary industry, tertiary industry, retail trade goods sales, urban construction land area, and total freight transport have a significant positive impact on socio-economic development; the correlation coefficient between the number of schools and the socio-economic development level index is negative. (3) Retail trade merchandise sales contribute the most to the Gini coefficient, where the percentage of secondary industry and urban construction land area have a higher cumulative contribution to growing cities (55.02%), the percentage of secondary industry has the lowest contribution to regenerating cities (10.94%), and the percentage of tertiary industry has an increasing contribution to declining cities year by year. Based on the above findings, some specific suggestions are provided to provide reference for resource-based city development planning.

Suggested Citation

  • Yiting Su & Jing Li & Shouqiang Yin & Jiabao Yue & Zhai Jiang & Tianyue Ma & Zhangqian Han, 2023. "Spatial and Temporal Variation Characteristics and Driving Mechanisms of Multidimensional Socio-Economic Development Levels in Resource-Based Cities," Sustainability, MDPI, vol. 15(2), pages 1-17, January.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:2:p:1573-:d:1034998
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    References listed on IDEAS

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