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Research on Sustainable Urban–Rural Integration Development: Measuring Levels, Influencing Factors, and Exploring Driving Mechanisms—Taking Eight Cities in the Greater Bay Area as Examples

Author

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  • Jing Xu

    (College of Marxism, Harbin Institute of Technology, Shenzhen 518055, China
    These authors contributed equally to this work.)

  • Zhenjian Zeng

    (College of Government, Sun Yat-Sen University, Guangzhou 510275, China
    These authors contributed equally to this work.)

  • Zhenhua Xi

    (College of Humanities and Development Studies, China Agricultural University, Beijing 100193, China)

  • Zhencong Peng

    (College of Marxism, East China University of Science and Technology, Shanghai 200237, China)

  • Gangheng Chen

    (College of Public Administration, South China Agricultural University, Guangzhou 510642, China)

  • Xiting Zhu

    (College of Chinese National Community, Northwest University for Nationalities, Lanzhou 730030, China)

  • Xinjia Chen

    (College of Natural Resources and Environment, Northwest A&F University, Xianyang 712199, China)

Abstract

Urban–rural integration is a top priority in social development and an urgent requirement for vigorously promoting rural revitalization. However, the current development of urban–rural integration in China still faces issues such as an unreasonable urban–rural industrial structure, unidirectional flow of rural population, and low sense of belonging among rural residents. Based on this, this paper selects eight cities from the Greater Bay Area as examples, organizing urban–rural integration development data from 1986 to 2022. It employs principal component analysis to quantitatively evaluate the evolutionary trend of urban–rural integration, constructs a fixed-effect panel quantile regression model to explore the factors influencing urban–rural integration and its spatiotemporal evolution, and uses threshold effects and interaction effects to test the threshold and application requirements for maximizing the benefits of urban–rural integration driving mechanisms, drawing empirical insights from comparisons with other bay areas around the world. The research found the following: First, between 1986 and 2022, the development of urban–rural integration in the Greater Bay Area steadily progressed, with gradually emerging effects, and industrial integration and population integration made significant contributions to the development of urban–rural integration. Second, the driving mechanisms of market economics, government intervention, and social fusion significantly impact urban–rural integration, with the influence of market economics being the most significant. Third, the impacts of the three driving mechanisms on urban–rural integration show temporal and spatial differences. In terms of time, market economics and government intervention always have a positive impact, while social fusion shows a “suppressing-promoting” trend, with a lower impact coefficient. Spatially, there are differences in strategies and priorities for promoting urban–rural integration in each region. Fourth, all three driving mechanisms exhibit threshold effects, and the explanatory power of any two interacting driving mechanisms for urban–rural integration development is stronger than that of any single mechanism. Notably, the combined interaction effect of the three driving mechanisms has the highest impact coefficient. The driving mechanisms should be implemented according to the principles of “synchronization, heterogeneity, and categorization”.

Suggested Citation

  • Jing Xu & Zhenjian Zeng & Zhenhua Xi & Zhencong Peng & Gangheng Chen & Xiting Zhu & Xinjia Chen, 2024. "Research on Sustainable Urban–Rural Integration Development: Measuring Levels, Influencing Factors, and Exploring Driving Mechanisms—Taking Eight Cities in the Greater Bay Area as Examples," Sustainability, MDPI, vol. 16(8), pages 1-29, April.
    • Handle: RePEc:gam:jsusta:v:16:y:2024:i:8:p:3357-:d:1377210
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    References listed on IDEAS

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    1. Plümper, Thomas & Troeger, Vera E., 2007. "Efficient Estimation of Time-Invariant and Rarely Changing Variables in Finite Sample Panel Analyses with Unit Fixed Effects," Political Analysis, Cambridge University Press, vol. 15(2), pages 124-139, April.
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    Cited by:

    1. Yuchen Lu & Jiakun Zhuang & Jun Chen & Chenlu Yang & Mei Kong, 2025. "The Impact of Farmland Transfer on Urban–Rural Integration: Causal Inference Based on Double Machine Learning," Land, MDPI, vol. 14(1), pages 1-30, January.

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