Abstract
One of the reasons cities are attractive is the rich array of urban amenities they provide. In this study, we examine how investments in urban rail transit affect clusters of consumer amenities in China, based on the experiences of six Chinese cities that are at varying stages of rail transit development. A difference-in-differences model is built using geocoded data on wide categories of consumer amenities to estimate the rail transit effects from a sample of 315 new rail transit stations opened between 2015 and 2018. Our results show that the opening of a new station induces an increase of approximately 4% in the total number of consumer amenities in the vicinity of the station within the first year. The impact strengthens substantially after two years. The effect of rail transit varies across cities and among different neighborhoods within a city. In most cities, rail transit effects are stronger in central areas, contributing to agglomeration of retail and dining amenities. When the rail system expands rapidly, however, the opening of a new station tiggers greater growth of daily service amenities in the suburbs, leading to a decentralization effect. Additionally, we find that neighborhood purchasing power and transit-adjacent land supply have limited impacts on amenity growth driven by new rail stations. This study provides important insight into how public amenities interact with private amenities and its findings have significant implications for urban planners and policymakers.
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Data Availability
The dataset of POI can be accessed at Amap (http://ditu.amap.com/) and raw data about rail transit investments in Chinese cities can be collected from the website of http://www.urbanrail.net/ and China’s Urban Rail Transit Annual Statistical Analysis Report.
Notes
This is included as an objective of Toronto’s 2041 Regional Transportation Plan (https://www.metrolinx.com/en/regionalplanning/rtp/).
This dataset has been increasingly applied in research for urban studies. For example, Li et al. (2018) estimated employment locations using POI data in Shanghai, China, while Long et al. (2022) analyzed the urban spatial structure by identifying diverse urban functions and human activities based on POI data in China.
In this study, education amenities only include private education facilities and organizations, such as private tutorial schools. Public education amenities are not included.
Note that, as Shenzhen only shows significant average effects in the mid-term and the mid-term estimation does not sacrifice any observation of newly opened stations, our investigations into the heterogeneity of Shenzhen’s rail transit effects will be based on the mid-term amenity status.
This is supported by the larger transaction volume of secondary housing in the suburban area than in the central area (Figure A1 of Appendices A).
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Acknowledgements
Kuang kuang Deng gratefully acknowledges financial support from the Pilot Research Project Funding for Young Scholars (Fund number: 2022110110) provided by the Shanghai University of Finance and Economics.We thank Prof. Chris Webster and Dr Özge Öner for their helpful comments and suggestions. All errors remain our own.
Funding
Kuangkuang Deng gratefully acknowledges financial support from the Pilot Research Project Funding for Young Scholars (Fund number: 2022110110) provided by the Shanghai University of Finance and Economics.
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Deng, K., Li, L. Rail transit and the growth of consumer amenities: evidence from Chinese cities. Transportation (2023). https://doi.org/10.1007/s11116-023-10379-z
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DOI: https://doi.org/10.1007/s11116-023-10379-z