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Does High-Speed Rail Influence Urban Dynamics and Land Pricing?

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  • Panrawee Rungskunroch

    (School of Civil Engineering, University of Birmingham, Birmingham B15 2TT, UK
    Birmingham Centre for Railway Research and Education (BCRRE), University of Birmingham, Birmingham B15 2TT, UK
    Institute of Transportation Study (ITS), University of California, Berkeley, CA 94720, USA)

  • Yuwen Yang

    (School of Civil Engineering, University of Birmingham, Birmingham B15 2TT, UK)

  • Sakdirat Kaewunruen

    (School of Civil Engineering, University of Birmingham, Birmingham B15 2TT, UK
    Birmingham Centre for Railway Research and Education (BCRRE), University of Birmingham, Birmingham B15 2TT, UK)

Abstract

At present, many countries around the world have significantly invested in sustainable transportation systems, especially for high-speed rail (HSR) infrastructures, since they are believed to improve economies, and regenerate regional and business growth. In this study, we focus on economic growth, dynamic land use, and urban mobility. The emphasis is placed on testing a hypothesis about whether HSRs can enable socio-economic development. Real case studies using big data from large cities in China, namely Shanghai province and Minhang districts, are taken into account. Socio-technical information such as employment rate, property pricing, and agglomeration in the country’s economy is collected from the China Statistics Bureau and the China Academy of Railway Sciences for analyses. This research aims to re-examine practical factors resulting from HSR’s impact on urban areas by using ANOVA analysis and dummy variable regression to analyse urban dynamics and property pricing. In addition, this study enhances the prediction outcomes that lead to urban planning strategies for the business area. The results reveal that there are various effects (i.e., regional accessibility, city development plans, and so on) required to enable the success of HSR infrastructure in order to enrich urban dynamics and land pricing. This paper also highlights critical perspectives towards sustainability, which are vital to social and economic impacts. In addition, this study provides crucial perspectives on sustainable developments for future HSR projects.

Suggested Citation

  • Panrawee Rungskunroch & Yuwen Yang & Sakdirat Kaewunruen, 2020. "Does High-Speed Rail Influence Urban Dynamics and Land Pricing?," Sustainability, MDPI, vol. 12(7), pages 1-18, April.
    • Handle: RePEc:gam:jsusta:v:12:y:2020:i:7:p:3012-:d:343288
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    Cited by:

    1. Shanlang Lin & Prithvi Raj Dhakal & Zhaowei Wu, 2021. "The Impact of High-Speed Railway on China’s Regional Economic Growth Based on the Perspective of Regional Heterogeneity of Quality of Place," Sustainability, MDPI, vol. 13(9), pages 1-24, April.
    2. Chao Ji & Yanke Yao & Jianqiang Duan & Wenxing Li, 2022. "Sustainable Mechanism of the Entrusted Transportation Management Mode on High-Speed Rail and the Impact of COVID-19: A Case Study of the Beijing–Shanghai High-Speed Rail," Sustainability, MDPI, vol. 14(3), pages 1-24, January.
    3. Najmeh Mozaffaree Pour & Tõnu Oja, 2021. "Prediction Power of Logistic Regression (LR) and Multi-Layer Perceptron (MLP) Models in Exploring Driving Forces of Urban Expansion to Be Sustainable in Estonia," Sustainability, MDPI, vol. 14(1), pages 1-22, December.
    4. Seham S. Al-Alola & Haya M. Alogayell & Ibtesam I. Alkadi & Soha A. Mohamed & Ismail Y. Ismail, 2021. "Recognition and Prediction of Land Dynamics and Its Associated Impacts in Al-Qurayyat City and along Al-Shamal Train Pathway in Saudi Arabia," Sustainability, MDPI, vol. 13(17), pages 1-25, September.
    5. Kyungtaek Kim & Junghoon Kim, 2020. "The Impact of High-Speed Railways on Unequal Accessibility Based on Ticket Prices in Korea," Sustainability, MDPI, vol. 12(16), pages 1-17, August.
    6. Sakdirat Kaewunruen & Shijie Peng & Olisa Phil-Ebosie, 2020. "Digital Twin Aided Sustainability and Vulnerability Audit for Subway Stations," Sustainability, MDPI, vol. 12(19), pages 1-17, September.
    7. Rungskunroch, Panrawee & Jack, Anson & Kaewunruen, Sakdirat, 2021. "Benchmarking on railway safety performance using Bayesian inference, decision tree and petri-net techniques based on long-term accidental data sets," Reliability Engineering and System Safety, Elsevier, vol. 213(C).

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