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Spatiotemporal Changes and Influencing Factors of the Coupled Production–Living–Ecological Functions in the Yellow River Basin, China

Author

Listed:
  • Zidao Lu

    (School of City and Environment, Northwestern University, Xi’an 710100, China)

  • Maomao Zhang

    (College of Public Administration, Huazhong University of Science and Technology, Wuhan 430079, China)

  • Chunguang Hu

    (School of Urban Planning and Design, Peking University, Shenzhen 518055, China)

  • Lianlong Ma

    (College of Public Administration, Huazhong University of Science and Technology, Wuhan 430079, China)

  • Enqing Chen

    (School of Education and Foreign Languages, Wuhan Donghu University, Wuhan 430212, China)

  • Cheng Zhang

    (School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan 430079, China)

  • Guozhen Xia

    (School of Electronic Information, Wuhan University, Wuhan 430079, China)

Abstract

The imbalance in the “production–living–ecology” function (PLEF) has become a major issue for global cities due to the rapid advancement of urbanization and industrialization worldwide. The realization of PLEF coupling and coordination is crucial for a region’s sustainable development. Existing research has defined the concept of PLEF from the perspective of land function and measured its coupling coordination level using relevant models. However, there is still room for improvement in the indicator system, research methods, and other aspects. This work builds a PLEF coupling coordination evaluation-index system based on the perspective of human habitat using multi-source data in order to examine the spatial differences in PLEF coupling coordination level and the influencing factors in the Yellow River Basin (YRB). Using the modified coupling coordination model, the Moran index, spatial Markov chain model, and geographically weighted random forest model were introduced to analyze its spatial and temporal differentiation and influencing factors. The results found that (a) the level of PLEF coupling coordination in the YRB from 2010 to 2022 has been improving, and the number of severely imbalanced cities has been reduced from 23 to 15, but the level of downstream cities’ coupling coordination is significantly higher than that of upstream cities. The probability of cities maintaining their own level is greater than 50%, and there is basically no cross-level transfer. (b) The Moran index of the PLEF coupling coordination level has risen from 0.137 to 0.229, which shows a significant positive clustering phenomenon and is continually strengthening. The intercity polarization effect is being continually enhanced as seen in the LISA clustering diagram. (c) There is significant heterogeneity between the influencing factors in time and space. In terms of importance level, the series is per capita disposable income (0.416) > nighttime lighting index (0.370) > local general public budget expenditure (0.332) > number of beds per 1000 people (0.191) > NO 2 content in the air (0.110). This study systematically investigates the dynamic evolution of the coupled coordination level of PLEF in the YRB and its influencing mechanism, which is of great practical use.

Suggested Citation

  • Zidao Lu & Maomao Zhang & Chunguang Hu & Lianlong Ma & Enqing Chen & Cheng Zhang & Guozhen Xia, 2024. "Spatiotemporal Changes and Influencing Factors of the Coupled Production–Living–Ecological Functions in the Yellow River Basin, China," Land, MDPI, vol. 13(11), pages 1-24, November.
    • Handle: RePEc:gam:jlands:v:13:y:2024:i:11:p:1909-:d:1520717
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    References listed on IDEAS

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    1. Yu Chen & Xuyang Su & Xuekai Wang, 2022. "Spatial Transformation Characteristics and Conflict Measurement of Production-Living-Ecology: Evidence from Urban Agglomeration of China," IJERPH, MDPI, vol. 19(3), pages 1-20, January.
    2. Liu, Runqiu & Zhang, Lie & Tang, Yudi & Jiang, Yongmu, 2024. "Understanding and evaluating the resilience of rural human settlements with a social-ecological system framework: The case of Chongqing Municipality, China," Land Use Policy, Elsevier, vol. 136(C).
    3. Wentao Niu & Jingyi Shi & Zhenzhen Xu & Tianxi Wang & Hexiong Zhang & Xiaoshan Su, 2022. "Evaluating the Sustainable Land Use in Ecologically Fragile Regions: A Case Study of the Yellow River Basin in China," IJERPH, MDPI, vol. 19(6), pages 1-23, March.
    4. Yafeng Zou & Chengfeng Yi & Yufei Rao & Feng Luo & Changhe Lv & Pinqi Wu, 2023. "Identify Optimization Type of Rural Settlements Based on “Production–Living–Ecological” Functions and Vitality: A Case Study of a Town in Northern China," Land, MDPI, vol. 12(10), pages 1-27, October.
    5. Zhang, Jing & Li, Sinan & Lin, Naifa & Lin, Yue & Yuan, Shaofeng & Zhang, Ling & Zhu, Jinxia & Wang, Ke & Gan, Muye & Zhu, Congmou, 2022. "Spatial identification and trade-off analysis of land use functions improve spatial zoning management in rapid urbanized areas, China," Land Use Policy, Elsevier, vol. 116(C).
    6. Xinghua Cui & Ning Xu & Wanxu Chen & Guanzheng Wang & Jiale Liang & Sipei Pan & Binqiao Duan, 2022. "Spatio-Temporal Variation and Influencing Factors of the Coupling Coordination Degree of Production-Living-Ecological Space in China," IJERPH, MDPI, vol. 19(16), pages 1-26, August.
    7. Maomao Zhang & Enqing Chen & Cheng Zhang & Chen Liu & Jianxing Li, 2024. "Multi-Scenario Simulation of Land Use Change and Ecosystem Service Value Based on the Markov–FLUS Model in Ezhou City, China," Sustainability, MDPI, vol. 16(14), pages 1-23, July.
    8. Fu, Jingying & Bu, Ziqiang & Jiang, Dong & Lin, Gang & Li, Xiang, 2022. "Sustainable land use diagnosis based on the perspective of production–living–ecological spaces in China," Land Use Policy, Elsevier, vol. 122(C).
    9. Tao Hong & Ningli Liang & Haomeng Li, 2023. "Study on the Spatial and Temporal Evolution Characteristics and Driving Factors of the “Production–Living–Ecological Space” in Changfeng County," Sustainability, MDPI, vol. 15(13), pages 1-15, July.
    10. Julie Le Gallo, 2004. "Space-Time Analysis of GDP Disparities among European Regions: A Markov Chains Approach," International Regional Science Review, , vol. 27(2), pages 138-163, April.
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