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Spatial Heterogeneity and Driving Mechanisms of Cultivated Land Intensive Utilization in the Beibu Gulf Urban Agglomeration, China

Author

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  • Zhongqiu Zhang

    (College of Geographical Sciences, Inner Mongolia Normal University, Hohhot 010022, China
    College of Resources and Environment, Beibu Gulf University, Qinzhou 535011, China
    Land Use and Remediation Engineering Technology Research Center of Inner Mongolia, Hohhot 010022, China)

  • Yufeng Zhang

    (College of Geographical Sciences, Inner Mongolia Normal University, Hohhot 010022, China
    Land Use and Remediation Engineering Technology Research Center of Inner Mongolia, Hohhot 010022, China)

  • Xiang Zhang

    (College of Geographical Sciences, Inner Mongolia Normal University, Hohhot 010022, China)

Abstract

Cultivated land intensive utilization (CLIU) exhibits spatial heterogeneity that is influenced by both natural and anthropogenic factors, with land dissected into different scale systems; however, CLIU has not yet been systematically explored. This study takes the Beibu Gulf urban agglomeration, a national-level model area for integrated land and sea development in China, as an example to investigate the spatial heterogeneity of CLIU and explore its driving factors through multiple econometrical and geographical methods, including identifying its underlying mechanisms. The results indicate that (1) the CLIU index is 0.334, its Gini coefficient is 0.183, and its comprehensive level has a low intensity and obvious spatial nonequilibrium characteristics. Hypervariable density (50.33%) and the intraprovincial gap (45.6%) are the main sources. (2) Among the independent effects of single factors, the multiple cropping index (0.57), labor force index (0.489), and intensification of construction land (0.375) exert the most influence on CLIU spatial variation. The interaction effects of two factors primarily manifested as nonlinear enhancements, with the interaction between the labor force index and multiple cropping index being particularly noteworthy (0.859). (3) The geographically weighted regression coefficients reveal that temperature (0.332), multiple cropping index (0.211), and labor force index (0.209) have relatively large and positive impacts on CLIU, while slope (−0.1), precipitation (−0.087), and population urbanization (−0.039) have relatively small and negative impacts; all factors exhibit spatial nonstationarity. The spatial heterogeneity of CLIU in the Beibu Gulf urban agglomeration is characterized by patterns’ nonequilibrium and factors’ nonstationarity. The driving mode of multiple factors on CLIU is manifested as follows: natural factors of cropland utilization provide basic guarantees, internal factors of CLIU provide positive enhancement, and external factors of land intensive utilization provide auxiliary promotion.

Suggested Citation

  • Zhongqiu Zhang & Yufeng Zhang & Xiang Zhang, 2024. "Spatial Heterogeneity and Driving Mechanisms of Cultivated Land Intensive Utilization in the Beibu Gulf Urban Agglomeration, China," Sustainability, MDPI, vol. 16(11), pages 1-25, May.
    • Handle: RePEc:gam:jsusta:v:16:y:2024:i:11:p:4565-:d:1403526
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    1. Dagum, Camilo, 1997. "A New Approach to the Decomposition of the Gini Income Inequality Ratio," Empirical Economics, Springer, vol. 22(4), pages 515-531.
    2. Sen, Amartya K, 1978. "On the Labour Theory of Value: Some Methodological Issues," Cambridge Journal of Economics, Cambridge Political Economy Society, vol. 2(2), pages 175-190, June.
    3. Xie, Hualin & Huang, Yingqian & Choi, Yongrok & Shi, Jiaying, 2021. "Evaluating the sustainable intensification of cultivated land use based on emergy analysis," Technological Forecasting and Social Change, Elsevier, vol. 165(C).
    4. Lai, Zhaohao & Chen, Meiqiu & Liu, Taoju, 2020. "Changes in and prospects for cultivated land use since the reform and opening up in China," Land Use Policy, Elsevier, vol. 97(C).
    5. Xie, Hualin & Chen, Qianru & Wang, Wei & He, Yafen, 2018. "Analyzing the green efficiency of arable land use in China," Technological Forecasting and Social Change, Elsevier, vol. 133(C), pages 15-28.
    6. Antony Trewavas, 2002. "Malthus foiled again and again," Nature, Nature, vol. 418(6898), pages 668-670, August.
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