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The Impact of Farmland Transfer on Urban–Rural Integration: Causal Inference Based on Double Machine Learning

Author

Listed:
  • Yuchen Lu

    (Business School, Beijing Normal University (BNU), Beijing 100875, China)

  • Jiakun Zhuang

    (Institute of Economic System and Management, Academy of Macroeconomic Research, Beijing 100035, China)

  • Jun Chen

    (School of Ethnology and Sociology, Minzu University of China (MUC), Beijing 100074, China)

  • Chenlu Yang

    (Business School, Beijing Normal University (BNU), Beijing 100875, China)

  • Mei Kong

    (China Institute of Education and Social Development, Beijing Normal University (BNU), Beijing 100875, China)

Abstract

Urban–rural fragmentation represents a significant challenge encountered by nations globally, particularly in both developing and developed contexts, during the modernisation process. This study examines the effects of rural land system reform on facilitating integrated development between urban and rural areas. The analysis of the impact of the 2010 liberalisation of the land transfer policy employs a dual machine learning model, utilising provincial-level data from China spanning 2005 to 2022, to address the limitations of traditional causal inference models while ensuring estimation accuracy. The findings indicate that the reform of the rural land system significantly enhances integrated urban–rural development, particularly in demographic, economic, and ecological dimensions. The mechanisms encompass the facilitation of extensive land and agricultural service operations, the development of new business entities, the migration of rural labour, and the enhancement of agricultural capital. Furthermore, notable disparities exist in the effects of reforms across various regions, particularly concerning urban–rural integration development and land transfer levels. The policy effects of land transfer exhibit a marginally diminishing trend. The influence of land transfer on urban–rural integration varies with economic development levels, demonstrating a nonlinear relationship, with the most pronounced effects observed in regions with moderate economic development. Additionally, the policy effects of land transfer differ based on geographic location. The impact of land transfer policies varies across geographic regions, with the central region exhibiting the most significant effect, followed by the north-eastern region, the western region, and the eastern region, which shows the least effect. This study provides a reference for advancing the reform of the marketisation of land factors, improving the efficiency of land resource allocation, and regionally and in multiple layers advancing the reform of the rural land system.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jlands:v:14:y:2025:i:1:p:148-:d:1565601
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    References listed on IDEAS

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    1. Victor Chernozhukov & Denis Chetverikov & Mert Demirer & Esther Duflo & Christian Hansen & Whitney Newey & James Robins, 2018. "Double/debiased machine learning for treatment and structural parameters," Econometrics Journal, Royal Economic Society, vol. 21(1), pages 1-68, February.
    2. Zhuoyuan Zhang, 2023. "Handbook of Chinese Economics," Springer Books, Springer, number 978-981-99-0420-4, March.
    3. Achim Ahrens & Christian B. Hansen & Mark E. Schaffer & Thomas Wiemann, 2024. "ddml: Double/debiased machine learning in Stata," Stata Journal, StataCorp LP, vol. 24(1), pages 3-45, March.
    4. Helmut Farbmacher & Martin Huber & Lukáš Lafférs & Henrika Langen & Martin Spindler, 2022. "Causal mediation analysis with double machine learning [Mediation analysis via potential outcomes models]," The Econometrics Journal, Royal Economic Society, vol. 25(2), pages 277-300.
    5. 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.
    6. Zhaoyang Liu & Samuel Esteban Rodríguez, 2024. "Research on the Interaction Mechanism between Land System Reform and Rural Population Flow: Europe (Taking Spain as an Example) and China," Land, MDPI, vol. 13(8), pages 1-19, July.
    7. Li, Bowei & Shen, Yueqin, 2021. "Effects of land transfer quality on the application of organic fertilizer by large-scale farmers in China," Land Use Policy, Elsevier, vol. 100(C).
    8. Kaili Peng & Chen Yang & Yao Chen, 2020. "Land transfer in rural China: incentives, influencing factors and income effects," Applied Economics, Taylor & Francis Journals, vol. 52(50), pages 5477-5490, October.
    9. Hao Liu & Gaojie Lu & Kui Luo & Huiming Zong, 2024. "Measurement and Spatio–Temporal Pattern Evolution of Urban–Rural Integration Development in the Chengdu-Chongqing Economic Circle," Land, MDPI, vol. 13(7), pages 1-19, June.
    10. Yangchenhao Wu & Wang Zhang, 2024. "The Impact of Land Transfer on Sustainable Agricultural Development from the Perspective of Green Total Factor Productivity," Sustainability, MDPI, vol. 16(16), pages 1-27, August.
    11. Yang, Jui-Chung & Chuang, Hui-Ching & Kuan, Chung-Ming, 2020. "Double machine learning with gradient boosting and its application to the Big N audit quality effect," Journal of Econometrics, Elsevier, vol. 216(1), pages 268-283.
    12. Yuyao Zuo & Chaoxian Yang & Guixin Xin & Ya Wu & Rongrong Chen, 2023. "Driving Mechanism of Comprehensive Land Consolidation on Urban–Rural Development Elements Integration," Land, MDPI, vol. 12(11), pages 1-19, November.
    13. Tianshu Quan & Weiguo Jia & Tianli Quan & Yuyun Xu, 2024. "Impact of Farmers’ Participation in the Transformation of the Farmland Transfer Market on the Adoption of Agricultural Green Production Technologies," Agriculture, MDPI, vol. 14(10), pages 1-23, September.
    14. Cai, Yunhao & Jing, Peng & Wang, Baihui & Jiang, Chengxi & Wang, Yuan, 2023. "How does “over-hype” lead to public misconceptions about autonomous vehicles? A new insight applying causal inference," Transportation Research Part A: Policy and Practice, Elsevier, vol. 175(C).
    15. Chen, Kunqiu & Long, Hualou & Liao, Liuwen & Tu, Shuangshuang & Li, Tingting, 2020. "Land use transitions and urban-rural integrated development: Theoretical framework and China’s evidence," Land Use Policy, Elsevier, vol. 92(C).
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