IDEAS home Printed from https://ideas.repec.org/a/gam/jlands/v10y2021i8p833-d610941.html
   My bibliography  Save this article

Spatial–Temporal Pattern and Influence Factors of Land Used for Transportation at the County Level since the Implementation of the Reform and Opening-Up Policy in China

Author

Listed:
  • Baochao Li

    (College of Geography and Planning, Sun Yat-Sen University, Guangzhou 510275, China)

  • Xiaoshu Cao

    (College of Geography and Planning, Sun Yat-Sen University, Guangzhou 510275, China
    Academy of Natural Resources and Territorial Space, Shaanxi Normal University, Xi’an 710119, China)

  • Jianbin Xu

    (College of Geography and Planning, Sun Yat-Sen University, Guangzhou 510275, China)

  • Wulin Wang

    (College of Environment and Resources, Fuzhou University, Fuzhou 350116, China)

  • Shishu Ouyang

    (College of Geography and Planning, Sun Yat-Sen University, Guangzhou 510275, China)

  • Dan Liu

    (College of Geography and Planning, Sun Yat-Sen University, Guangzhou 510275, China)

Abstract

In this paper, we study the characteristics of the spatial–temporal pattern of land used for transportation at the county level since the implementation of the reform and opening-up policy in China and discuss the factors that influence the spatial differences between lands used for transportation in order to provide a reference for the formulation of traffic policies. The authors used ArcGIS spatial analysis, an ordinary least squares (OLS) regression model, and a geographic detector model based on the data of the transportation network at the county level in China from 1978 to 2018. We obtained the following results: (1) The land used for transportation at the county level in China is divided by the Hu Huanyong Line, which is characterized by spatial variation, where the southeastern region is higher than the northwestern region. (2) Counties with a high proportion of land used for transportation show obvious changes, characterized by the transformation from the “corridor” zonal distribution of arteries to the “diamond” group distribution of major city clusters, reducing the gap in land used for transportation at the county level in China. (3) The level of industrialization, per capita gross regional product (PGRP), and ratio of the non-agricultural working population all have an incentivizing impact on the increase in land used for transportation at the county level in China. We conclude that the land used for transportation at the county level in China is jointly decided by the economy, industry, and population. Therefore, we believe that it is necessary to promote fast economic growth, the upgrading of industrial structures, and population density to achieve the balanced development of land used for transportation at the county level in China.

Suggested Citation

  • Baochao Li & Xiaoshu Cao & Jianbin Xu & Wulin Wang & Shishu Ouyang & Dan Liu, 2021. "Spatial–Temporal Pattern and Influence Factors of Land Used for Transportation at the County Level since the Implementation of the Reform and Opening-Up Policy in China," Land, MDPI, vol. 10(8), pages 1-17, August.
  • Handle: RePEc:gam:jlands:v:10:y:2021:i:8:p:833-:d:610941
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2073-445X/10/8/833/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2073-445X/10/8/833/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Wang, Ling & Wang, Ke & Zhang, Jianjun & Zhang, Di & Wu, Xia & Zhang, Lijun, 2020. "Multiple objective-oriented land supply for sustainable transportation: A perspective from industrial dependence, dominance and restrictions of 127 cities in the Yangtze River Economic Belt of China," Land Use Policy, Elsevier, vol. 99(C).
    2. Jiao, Jingjuan & Wang, Jiaoe & Jin, Fengjun, 2017. "Impacts of high-speed rail lines on the city network in China," Journal of Transport Geography, Elsevier, vol. 60(C), pages 257-266.
    3. Biao Yin & Liu Liu & Nicolas Coulombel & Vincent Viguie, 2017. "Evaluation of ridesharing impacts using an integrated transport land-use model: a case study for the Paris region," Post-Print hal-01695081, HAL.
    4. Or Levkovich & Jan Rouwendal & Jos van Ommeren, 2020. "The impact of highways on population redistribution: the role of land development restrictions [Roads and innovation]," Journal of Economic Geography, Oxford University Press, vol. 20(3), pages 783-808.
    5. Holz-Rau, Christian & Scheiner, Joachim, 2019. "Land-use and transport planning – A field of complex cause-impact relationships. Thoughts on transport growth, greenhouse gas emissions and the built environment," Transport Policy, Elsevier, vol. 74(C), pages 127-137.
    6. Cuauhtemoc Anda & Alexander Erath & Pieter Jacobus Fourie, 2017. "Transport modelling in the age of big data," International Journal of Urban Sciences, Taylor & Francis Journals, vol. 21(0), pages 19-42, August.
    7. Lei Zhou & Feng Zhen & Yiqing Wang & Liyang Xiong, 2019. "Modeling the Spatial Formation Mechanism of Poverty-Stricken Counties in China by Using Geographical Detector," Sustainability, MDPI, vol. 11(17), pages 1-20, August.
    8. van Geet, Marijn Thomas & Lenferink, Sander & Arts, Jos & Leendertse, Wim, 2019. "Understanding the ongoing struggle for land use and transport integration: Institutional incongruence in the Dutch national planning process," Transport Policy, Elsevier, vol. 73(C), pages 84-100.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Luhui Qi & Liqi Jia & Yubin Luo & Yuanyi Chen & Minggang Peng, 2022. "The External Characteristics and Mechanism of Urban Road Corridors to Agglomeration: Case Study for Guangzhou, China," Land, MDPI, vol. 11(7), pages 1-17, July.
    2. Liangen Zeng & Haitao Li & Xiao Wang & Zhao Yu & Haoyu Hu & Xinyue Yuan & Xuhai Zhao & Chengming Li & Dandan Yuan & Yukun Gao & Yang Nie & Liangzhen Huang, 2022. "China’s Transport Land: Spatiotemporal Expansion Characteristics and Driving Mechanism," Land, MDPI, vol. 11(8), pages 1-18, July.
    3. Peichao Dai & Ruxu Sheng & Zhongzhen Miao & Zanxu Chen & Yuan Zhou, 2021. "Analysis of Spatial–Temporal Characteristics of Industrial Land Supply Scale in Relation to Industrial Structure in China," Land, MDPI, vol. 10(11), pages 1-18, November.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Anastasiadou, Konstantina & Gavanas, Nikolaos, 2023. "Enhancing urban public space through appropriate sustainable mobility policies. A multi-criteria analysis approach," Land Use Policy, Elsevier, vol. 132(C).
    2. Yang, Zhiwei & Li, Can & Jiao, Jingjuan & Liu, Wei & Zhang, Fangni, 2020. "On the joint impact of high-speed rail and megalopolis policy on regional economic growth in China," Transport Policy, Elsevier, vol. 99(C), pages 20-30.
    3. Kevin Credit & Zander Arnao, 2023. "A method to derive small area estimates of linked commuting trips by mode from open source LODES and ACS data," Environment and Planning B, , vol. 50(3), pages 709-722, March.
    4. Pieroni, Caio & Giannotti, Mariana & Alves, Bianca B. & Arbex, Renato, 2021. "Big data for big issues: Revealing travel patterns of low-income population based on smart card data mining in a global south unequal city," Journal of Transport Geography, Elsevier, vol. 96(C).
    5. O'Driscoll, Conor & Crowley, Frank & Doran, Justin & McCarthy, Nóirín, 2022. "Retail sprawl and CO2 emissions: Retail centres in Irish cities," Journal of Transport Geography, Elsevier, vol. 102(C).
    6. Wu, Bingyu & Levinson, David M., 2024. "A multi-modal analysis of the effect of transport on population and productivity in China," Journal of Transport Geography, Elsevier, vol. 116(C).
    7. Wu, Rong & Li, Yingcheng & Wang, Shaojian, 2022. "Will the construction of high-speed rail accelerate urban land expansion? Evidences from Chinese cities," Land Use Policy, Elsevier, vol. 114(C).
    8. Chen, Zhuo & Wang, Jiaoe & Li, Yongling, 2022. "Intercity connections by expressway in metropolitan areas: Passenger vs. cargo flow," Journal of Transport Geography, Elsevier, vol. 98(C).
    9. Jinping Lin & Kangmin Wu, 2023. "Intercity asymmetrical linkages influenced by Spring Festival migration and its multivariate distance determinants: a case study of the Yangtze River Delta Region in China," Palgrave Communications, Palgrave Macmillan, vol. 10(1), pages 1-10, December.
    10. Hiramatsu, Tomoru, 2023. "Inter-metropolitan regional migration galvanized by high-speed rail: A simulation analysis of the Linear Chuo Shinkansen line in Japan," Socio-Economic Planning Sciences, Elsevier, vol. 85(C).
    11. Hongchang Li & Jack Strauss & Lihong Liu, 2019. "A Panel Investigation of High-Speed Rail (HSR) and Urban Transport on China’s Carbon Footprint," Sustainability, MDPI, vol. 11(7), pages 1-24, April.
    12. Nasir, Muhammad Ali & Al-Emadi, Ahmed Abdulsalam & Shahbaz, Muhammad & Hammoudeh, Shawkat, 2019. "Importance of oil shocks and the GCC macroeconomy: A structural VAR analysis," Resources Policy, Elsevier, vol. 61(C), pages 166-179.
    13. Yu Jia & Yunqian Wang & Piao Li & Shuang Gao, 2024. "Economic Communication: The Influence of High-Speed Rail on Urban-Rural Income Inequality in China," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 174(1), pages 47-73, August.
    14. Li, Jing & Yu, Qian & Ma, Ding, 2024. "Does China's high-speed rail network promote inter-city technology transfer? ——A multilevel network analysis based on the electronic information industry," Transport Policy, Elsevier, vol. 145(C), pages 11-24.
    15. Bantis, Thanos & Haworth, James, 2020. "Assessing transport related social exclusion using a capabilities approach to accessibility framework: A dynamic Bayesian network approach," Journal of Transport Geography, Elsevier, vol. 84(C).
    16. Hans R A Koster, 2024. "The Welfare Effects of Greenbelt Policy: Evidence from England," The Economic Journal, Royal Economic Society, vol. 134(657), pages 363-401.
    17. Gleb V. Savin, 2021. "The smart city transport and logistics system: Theory, methodology and practice," Upravlenets, Ural State University of Economics, vol. 12(6), pages 67-86, October.
    18. Huang, Yan & Zong, Huiming, 2022. "The intercity railway connections in China: A comparative analysis of high-speed train and conventional train services," Transport Policy, Elsevier, vol. 120(C), pages 89-103.
    19. María Vega-Gonzalo & Panayotis Christidis, 2022. "Fair Models for Impartial Policies: Controlling Algorithmic Bias in Transport Behavioural Modelling," Sustainability, MDPI, vol. 14(14), pages 1-23, July.
    20. Haoran Zhang & Ying Chai & Xuyu Yang & Wenli Zhao, 2022. "High-Speed Rail and Urban Growth Disparity: Evidence from China," Sustainability, MDPI, vol. 14(13), pages 1-13, July.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jlands:v:10:y:2021:i:8:p:833-:d:610941. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.