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Characteristics of Spatial–Temporal Evolution of Sustainable Intensification of Cultivated Land Use and Analysis of Influencing Factors in China, 2001–2020

Author

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  • Guiying Liu

    (School of Economics and Management, Jiangxi Agricultural University, Nanchang 330045, China)

  • Mengqi Yang

    (School of Economics and Management, Jiangxi Agricultural University, Nanchang 330045, China)

Abstract

The rapid growth of the global population, the acceleration of the urbanization process, and the demands of economic development, place enormous pressure on scarce land resources. Cultivated land use presents a series of problems, hindering its socioeconomic and ecological sustainability. The sustainable intensification of cultivated land use (SICLU) is a development model designed to maximize land use efficiency, while minimizing environmental pollution. It is considered to be an efficient method to achieve three aspects of sustainable goals, namely in regard to society, the economy, and ecology, simultaneously. This approach has significant theoretical and practical implications for China’s food security and ecological safety. This study incorporates the “agricultural carbon emissions” indicator into the indicator evaluation system. Using the super-efficiency SBM model, we estimate the SICLU levels in China from 2001 to 2020. ArcGIS and the Dagum Gini coefficient decomposition model are employed to explore the temporal and spatial evolution characteristics and non-equilibrium spatial dynamics of SICLU in China. Finally, the Tobit regression model is used to reveal the driving factors. The results show the following: (1) Since 2003, China’s SICLU levels demonstrate an overall ascent amid fluctuations, sustaining a relatively high average annual level of 0.945. (2) In terms of spatial evolution patterns, China’s SICLU levels demonstrate significant spatial disparities, with distinct differences among the four major regions. Regions with similar SICLU levels show a certain degree of spatial adjacency. (3) There are significant regional disparities in China’s SICLU levels, which overall exhibit a declining trend. The differences between regions are the primary source of spatial variation, followed by hypervariable density and intra-regional disparities. (4) The regional industrial structure, the level of agricultural modernization, the agricultural cropping structure, and the per capita sown area, positively influence the enhancement of SICLU levels in China. Throughout the study period, the SICLU levels in China continuously improved and the overall regional disparities diminished. However, significant inter-regional imbalances persist, necessitating tailored optimization measures, based on local conditions. Establishing a coordinated mechanism for orderly and synergistic regional development is crucial, in order to provide references to decision-makers to promote the rational use of arable land in China.

Suggested Citation

  • Guiying Liu & Mengqi Yang, 2024. "Characteristics of Spatial–Temporal Evolution of Sustainable Intensification of Cultivated Land Use and Analysis of Influencing Factors in China, 2001–2020," Sustainability, MDPI, vol. 16(23), pages 1-22, December.
    • Handle: RePEc:gam:jsusta:v:16:y:2024:i:23:p:10679-:d:1537578
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    References listed on IDEAS

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    1. Per Andersen & Niels Christian Petersen, 1993. "A Procedure for Ranking Efficient Units in Data Envelopment Analysis," Management Science, INFORMS, vol. 39(10), pages 1261-1264, October.
    2. Jie Li & Zhengchuan Sun & Jie Zhou & Yaya Sow & Xufeng Cui & Haipeng Chen & Qianling Shen, 2023. "The Impact of the Digital Economy on Carbon Emissions from Cultivated Land Use," Land, MDPI, vol. 12(3), pages 1-18, March.
    3. Chaofan Ma & Lingzhi Wang & Yangfan Chen & Junjie Wu & Anqi Liang & Xinyao Li & Chengge Jiang & Hichem Omrani, 2024. "Evolution and Drivers of Production Patterns of Major Crops in Jilin Province, China," Land, MDPI, vol. 13(7), pages 1-19, July.
    4. Liyuan Zhang & Xiang Ma & Young-Seok Ock & Lingli Qing, 2022. "Research on Regional Differences and Influencing Factors of Chinese Industrial Green Technology Innovation Efficiency Based on Dagum Gini Coefficient Decomposition," Land, MDPI, vol. 11(1), pages 1-20, January.
    5. Yajuan Wang & Xi Wu & Hongbo Zhu, 2022. "Spatio-Temporal Pattern and Spatial Disequilibrium of Cultivated Land Use Efficiency in China: An Empirical Study Based on 342 Prefecture-Level Cities," Land, MDPI, vol. 11(10), pages 1-15, October.
    6. Dagum, Camilo, 1997. "A New Approach to the Decomposition of the Gini Income Inequality Ratio," Empirical Economics, Springer, vol. 22(4), pages 515-531.
    7. Xiao Lu & Yi Qu & Piling Sun & Wei Yu & Wenlong Peng, 2020. "Green Transition of Cultivated Land Use in the Yellow River Basin: A Perspective of Green Utilization Efficiency Evaluation," Land, MDPI, vol. 9(12), pages 1-22, November.
    8. Jinhao Bao & Sucheng Xu & Wu Xiao & Jiang Wu & Tie Tang & Heyu Zhang, 2024. "Spatial Differentiation and Environmental Controls of Land Consolidation Effectiveness: A Remote Sensing-Based Study in Sichuan, China," Land, MDPI, vol. 13(7), pages 1-18, July.
    9. Kotu, Bekele Hundie & Oyinbo, Oyakhilomen & Hoeschle-Zeledon, Irmgard & Nurudeen, Abdul Rahman & Kizito, Fred & Boyubie, Benedict, 2022. "Smallholder farmers’ preferences for sustainable intensification attributes in maize production: Evidence from Ghana," World Development, Elsevier, vol. 152(C).
    10. Yufei Wang & Shuang Liang & Yuxin Liang & Xiaoxue Liu, 2024. "A Comprehensive Accounting of Carbon Emissions and Carbon Sinks of China’s Agricultural Sector," Land, MDPI, vol. 13(9), pages 1-23, September.
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