The Impacts of High-Speed Rail on Regional Accessibility and Spatial Development—Updated Evidence from China’s Mid-Yangtze River City-Cluster Region
Abstract
:1. Introduction
2. Literature Review
2.1. HSR Impacts on Travel Time
2.2. HSR Impacts on Accessibility
2.3. HSR Impacts on Spatial Development
3. Methodology
3.1. Study Case and Data
3.1.1. The Mid-Yangtze River City-Cluster Region
3.1.2. Data on the Socioeconomics and Travel Times
3.2. Analytical Methods
3.2.1. Travel Times
3.2.2. Accessibility
3.2.3. Inequality Measured by Coefficient of Variation (CV)
3.2.4. Economic Linkage
4. Results
4.1. Travel Time Impacts of HSR
- The overall average travel time at the city-cluster level decreased by more than two hours (124 min), from 359 min in 2006 to 235 min in 2014, a 34.5% reduction. HSR shortened the time distance between MYRCCR cities and shrank their spatial separation.
- Cities with HSR stations enjoyed greater time savings (150 min on average) than other cities. Non-rail cities also benefited by having an average time reduction of 121 min because the residents in these cities could access the HSR stations in the neighboring cities. The counties/cities receiving the least time-saving benefits (i.e., the Bottom 10 shown in Table 1) are mostly those located in the remote areas of the region. For instance, Suichuan, Xiushui, and Jinggangshan, had travel time reductions from 2006 to 2014 by 16.9%, 18.5%, and 19.1%, respectively. This result raises concerns over possible increase in spatial inequality associated with HSR services between the Bottom 10 and the Top 10 groups of cities.
- The biggest winners on travel time savings were those cities with newly built HSR stations, for example, Nanfeng, Qidong, Qianjiang, and Xiantao. This is due to the fact that these cities had no rail connection at all in 2006. Being connected with HSR gave them a bigger jump in time reductions than those previously connected by the conventional rail.
4.2. Accessibility Effects of HSR
- Accessibility improved all over the city-cluster, at the average level of increase by 36.1% from 2006 to 2014. HSR cities enjoyed much greater improvement on accessibility than the non-HSR cities. Specifically, the accessibility of HSR cities increased by 64.3%, well above that (about 25%) for cities without HSR or any rail connections.
- The gap in accessibility increment (ranging from 2.2% to 128%) between the top 10 and the bottom 10 group of cities was much bigger than that in travel time savings (ranging from 16.9% to 54%) noted in the previous section. Furthermore, the cities in the top 10 and the bottom 10 groups of accessibility changes differed from those two respective groups of travel time savings. This result confirms that travel time savings are beneficial but the measure does not capture the broader impacts of HSR on the wellbeing of people and places. Accessibility, which is determined jointly by transportation services and the distribution of opportunities, presents a more informative measure than travel times to policy discussions on the implications of major transportation investments, i.e., HSR in the current discussion.
4.3. Equality Impacts of HSR
4.4. HSR Effects on Inter-City Economic Linkage
4.4.1. The Network-Level Economic Linkage
4.4.2. The Pattern of Spatial Linkage
5. Discussion and Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Regions | T2006 | T2014 | Change 14-06 | (%) |
---|---|---|---|---|
Mid-Yangtze River | 359 | 235 | −124 | 34.5 |
HSR cities (40) | 327 | 177 | −150 | 45.9 |
Conventional rail cities (57) | 336 | 210 | −126 | 37.5 |
No-rail cities (88) | 383 | 262 | −121 | 31.6 |
Top 10 | ||||
Qidong | 400 | 184 | −216 | 54.0 |
Leiyang | 393 | 182 | −211 | 53.7 |
Hanchuan | 358 | 173 | −185 | 51.7 |
Xiantao | 374 | 183 | −192 | 51.3 |
Hengyang | 348 | 171 | −177 | 50.9 |
Nanfeng | 428 | 212 | −217 | 50.7 |
Hengshan | 332 | 166 | −166 | 50.0 |
Gaoan | 322 | 162 | −160 | 49.7 |
Yueyang | 299 | 153 | −146 | 48.8 |
Qianjiang | 382 | 195 | −187 | 49.0 |
Bottom 10 | ||||
Suichuan | 435 | 372 | −63 | 16.9 |
Xiushui | 353 | 298 | −55 | 18.5 |
Jinggangshan | 381 | 320 | −61 | 19.1 |
Wan’an | 421 | 348 | −74 | 21.3 |
Baokang | 543 | 434 | −109 | 25.1 |
Tonggu | 381 | 302 | −79 | 26.2 |
Taihe | 356 | 281 | −74 | 26.3 |
Ji’an | 347 | 273 | −74 | 27.1 |
Yicheng | 462 | 362 | −99 | 27.3 |
Le’an | 381 | 299 | −82 | 27.4 |
Regions | PA2006 | PA2014 | Change 2014-06 | Change (%) |
---|---|---|---|---|
Mid-Yangtze River | 82.9 | 112.8 | 29.9 | 36.1 |
HSR cities (40) | 103.7 | 170.4 | 66.7 | 64.3 |
Conventional rail cities (57) | 86.3 | 108.2 | 21.9 | 25.4 |
No rail cities (88) | 71.2 | 89.6 | 18.4 | 25.8 |
Top10 | ||||
Hengshan | 77.3 | 176.2 | 99.0 | 128.0 |
Xianning | 85.5 | 178.2 | 92.6 | 108.3 |
Chibi | 82.1 | 170.0 | 87.9 | 107.0 |
Zhuzhou | 127.7 | 256.1 | 128.4 | 100.5 |
Miluo | 97.7 | 195.2 | 97.4 | 99.7 |
Hanchuan | 97.3 | 188.6 | 91.3 | 93.8 |
Nanfeng | 47.4 | 91.2 | 43.9 | 92.6 |
Hengyang | 105.2 | 198.1 | 92.9 | 88.3 |
Nancheng | 54.1 | 99.7 | 45.5 | 84.1 |
Yueyang | 111.1 | 204.4 | 93.2 | 83.9 |
Bottom 10 | ||||
Poyang | 110.3 | 112.6 | 2.4 | 2.2 |
Li | 88.1 | 91.3 | 3.2 | 3.6 |
Taoyuan | 90.2 | 94.0 | 3.8 | 4.3 |
Xinhua | 109.3 | 114.1 | 4.8 | 4.4 |
Shimen | 79.4 | 83.2 | 3.8 | 4.8 |
Xiushui | 79.0 | 83.2 | 4.3 | 5.4 |
Jianli | 107.7 | 115.3 | 7.5 | 7.0 |
Anhua | 85.4 | 92.6 | 7.2 | 8.5 |
Suichuan | 58.8 | 63.8 | 5.0 | 8.5 |
Liuyang | 112.5 | 124.7 | 12.3 | 10.9 |
Regions | CV2006 | CV2014 | (%) |
---|---|---|---|
Mid-Yangtze River | 36.1 | 42.6 | 17.7 |
HSR cities (40) | 43.2 | 36.1 | −16.4 |
Conventional rail cities (57) | 24.8 | 25.8 | 3.6 |
No rail cities (88) | 26.4 | 27.2 | 2.7 |
Prefecture-level Cities | |||
Xianning | 14.5 | 37.3 | 157.2 |
Yueyang | 10.9 | 26.7 | 145.0 |
Xiaogan | 12.4 | 27.8 | 124.2 |
Huangshi | 2.7 | 5.2 | 92.6 |
Changsha | 27.6 | 46.7 | 69.2 |
Zhuzhou | 33.1 | 56 | 69.2 |
Jiujiang | 16.9 | 26.4 | 56.2 |
Hengyang | 13.7 | 20.5 | 49.6 |
Xinyu | 15.6 | 22.8 | 46.2 |
Pingxiang | 27.1 | 36.5 | 34.7 |
Jingzhou | 17.5 | 22.6 | 29.1 |
Huanggang | 20 | 23.2 | 16.5 |
Ji’an | 13.7 | 15.7 | 14.6 |
Yichang | 36.9 | 41.9 | 13.6 |
Xiangtan | 21 | 23.6 | 12.9 |
Yiyang | 18.7 | 20.7 | 10.7 |
Changde | 22 | 23.9 | 8.6 |
Yichun | 35.9 | 38.4 | 7.0 |
Xiangyang | 47 | 47.2 | 0.4 |
Wuhan | 84.5 | 84.4 | −0.1 |
Nanchang | 45.2 | 44.3 | −2.0 |
Fuzhou | 31.1 | 29 | −6.8 |
Jingdezhen | 23.2 | 21.5 | −7.3 |
Shangrao | 24.2 | 20.1 | −16.9 |
Jingmen | 14.4 | 11.1 | −22.2 |
Loudi | 20.6 | 15.3 | −25.2 |
Yingtan | 11.8 | 6.8 | −43.2 |
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Liu, L.; Zhang, M. The Impacts of High-Speed Rail on Regional Accessibility and Spatial Development—Updated Evidence from China’s Mid-Yangtze River City-Cluster Region. Sustainability 2021, 13, 4227. https://doi.org/10.3390/su13084227
Liu L, Zhang M. The Impacts of High-Speed Rail on Regional Accessibility and Spatial Development—Updated Evidence from China’s Mid-Yangtze River City-Cluster Region. Sustainability. 2021; 13(8):4227. https://doi.org/10.3390/su13084227
Chicago/Turabian StyleLiu, Liwen, and Ming Zhang. 2021. "The Impacts of High-Speed Rail on Regional Accessibility and Spatial Development—Updated Evidence from China’s Mid-Yangtze River City-Cluster Region" Sustainability 13, no. 8: 4227. https://doi.org/10.3390/su13084227
APA StyleLiu, L., & Zhang, M. (2021). The Impacts of High-Speed Rail on Regional Accessibility and Spatial Development—Updated Evidence from China’s Mid-Yangtze River City-Cluster Region. Sustainability, 13(8), 4227. https://doi.org/10.3390/su13084227