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Mitigation Strategy of Land Use Mix for Jobs-Housing Mismatch

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  • Zhuangtian Liu

    (School of Public Administration, Zhejiang University of Finance and Economics, Hangzhou 310018, China)

  • Shaohua Wu

    (China Institute of Regulation and Public Policy Research, Zhejiang University of Finance and Economics, Hangzhou 310018, China
    Zhejiang Institute of “Eight–Eight” Strategies, Hangzhou 310018, China)

  • Canying Zeng

    (School of Public Administration, Zhejiang University of Finance and Economics, Hangzhou 310018, China)

  • Yunxiao Dang

    (School of Public Administration, Zhejiang University of Finance and Economics, Hangzhou 310018, China)

Abstract

The jobs-housing mismatch phenomenon in urban China stems from the combined effects of housing commodification and the improvement of transportation infrastructure. These factors have contributed to the emergence of lengthy commutes and a range of urban challenges. This study examines the issue of jobs-housing mismatch in large cities, focusing on Hangzhou. It utilizes mobile signaling big data, geographically weighted regression, and spatial analysis to investigate the link between land mixed-use and this mismatch. The results reveal that Hangzhou faces a significant residential-employment mismatch, particularly in a ring-like pattern. Central urban areas are relatively balanced, while residential areas band around the center, and employment areas are scattered both centrally and on the outskirts. Land mixed-use impacts this mismatch spatially. In new developments, increased land use mix exacerbates the mismatch, while in ecological green spaces, it has a suppressive effect. Based on these findings, Hangzhou’s main urban area is divided into nine zones, each with tailored suggestions for balancing residential and employment spaces. This study demonstrates that mobile signaling data can precisely capture micro-level characteristics of residential and employment patterns. A multi-dimensional approach to land mixed-use offers a more comprehensive understanding than a single perspective. The zoning strategy helps establish spatial differences and balance residential-employment relations, providing valuable insights for urban renewal and land function optimization.

Suggested Citation

  • Zhuangtian Liu & Shaohua Wu & Canying Zeng & Yunxiao Dang, 2025. "Mitigation Strategy of Land Use Mix for Jobs-Housing Mismatch," Land, MDPI, vol. 14(1), pages 1-18, January.
    • Handle: RePEc:gam:jlands:v:14:y:2025:i:1:p:82-:d:1559834
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    References listed on IDEAS

    as
    1. Peihong Wang & Kerun Li & Wenbing Zhang, 2024. "China’s New Housing Security Model: Evaluation of the Job–Housing Balance in Affordable Rental Housing, Shanghai," Land, MDPI, vol. 13(7), pages 1-26, July.
    2. Tao, Yinhua & van Ham, Maarten & Petrović, Ana & Ta, Na, 2023. "A household perspective on the commuting paradox: Longitudinal relationships between commuting time and subjective wellbeing for couples in China," Transportation Research Part A: Policy and Practice, Elsevier, vol. 170(C).
    3. Zhou, Xingang & Yeh, Anthony G.O. & Yue, Yang, 2018. "Spatial variation of self-containment and jobs-housing balance in Shenzhen using cellphone big data," Journal of Transport Geography, Elsevier, vol. 68(C), pages 102-108.
    4. repec:cdl:uctcwp:qt7mx3k73h is not listed on IDEAS
    5. Li, Si-ming & Liu, Yi, 2016. "The jobs-housing relationship and commuting in Guangzhou, China: Hukou and dual structure," Journal of Transport Geography, Elsevier, vol. 54(C), pages 286-294.
    6. Chen, Ruoyu & Zhang, Min & Zhou, Jiangping, 2023. "Jobs-housing relationships before and amid COVID-19: An excess-commuting approach," Journal of Transport Geography, Elsevier, vol. 106(C).
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