The associations of newly launched high-speed rail stations with industrial gentrification

Journal of Transport Geography 83 (2020) 102662

Contents lists available at ScienceDirect

Journal of Transport Geography journal homepage: www.elsevier.com/locate/jtrangeo

The associations of newly launched high-speed rail stations with industrial gentrification

T

⁎

Jen-Jia Lina, , Ze-Xing Xieb a b

Department of Geography, National Taiwan University; No. 1, Sec. 4, Roosevelt Road, Taipei 10617, Taiwan Graduate Institute of Building and Planning, National Taiwan University, Taiwan

A R T I C LE I N FO

A B S T R A C T

Keywords: Gentrification High-speed rail Cox proportional hazard regression

Industrial gentrification occurs when lower-skill or lower-wage industries are displaced by higher-skill or higherwage industries caused by various driving forces. This research explored the associations of newly launched high-speed rail (HSR) stations with industrial gentrification. Sample data were obtained from business registration records from 2010 to 2018 for areas surrounding the Hangzhou East Railway Station (a new HSR station in newly developing areas) and Hangzhou Railway Station (a pre-existing rail station in developed areas) in China. Cox proportional hazard regressions were applied to analyze the survival risks of businesses. Empirical results suggest that the newly launched HSR services have induced industrial gentrification in the developed station area. Except for the displacement of agricultural production activities, HSR-induced industrial gentrification has not yet been manifested in the newly developed station area. The latter phenomenon is because of the sufficiently available lands and floor spaces for industrial development and the lower-skill or lower-wage industries and higher-skill or higher-wage industries that benefit from HSR services in the form of revenues.

1. Introduction High-speed rail (HSR) is a type of rail transport that operates substantially faster than conventional rail systems. The International Union of Railways states that a railway system that operates trains in speeds of more than 200 km/h is recognized as an HSR system. The first HSR system began operations in Japan in 1964. Subsequently, HSR systems have been successively equipped in Japan and Europe and have been vigorously developed in East Asia after 2000. More than 45,000 km of HSR lines operate worldwide at the end of 2017 (UIC, 2018), and the total network length is continuously growing. The China Railway High-speed (CRH) is the most remarkable among the existing HSR systems worldwide. The CRH officially started its services in 2008, and its current HSR operation deploys the largest system, as evidenced by more than 25,000 km of network length and service provision of approximately 65% of the total passenger-kilometers of the world (UIC, 2018). In the past decade, the intensively expanding HSR network in mainland China has changed intercity accessibility at the national scale, which is a phenomenon that can result in substantial impacts on socioeconomic developments within and surrounding HSR station areas. However, the HSR impacts on station area development at the local scale have been ignored in the existing HSR literature. Previous impact studies on the CRH have mostly focused ⁎

at either regional or national scale and used cities as units of analysis. These studies include those that explored the impacts of CRH on accessibility (Cao et al., 2013; Shaw et al., 2014; Wang et al., 2016; Zhang et al., 2016), economic development (Cheng et al., 2015; Chen and Haynes, 2017; Chen et al., 2016), and domestic aviation (Zhang et al., 2017; Yang and Zhang, 2012; Chen, 2017; Fu et al., 2012). These regional-scale and national-scale studies have provided meaningful aggregate findings on city and regional developments, but have failed to explain the influences of HSR stations on nearby area developments. The case study of the Hangzhou East Railway Station (HERS) area by Chen and Wei (2013), which is regarded a rare work on CRS impact research at the local scale, raised three social issues on the basis of the observation of the HERS area when the station began to launch its HSR services. However, the observation period should be extended for the local-scale research of HSR impacts. Feng et al. (2018) reviewed the literature on HSR impacts in East Asia and provided general information about socioeconomic changes associated with the station areas. Studies in Japan (Haynes, 1997a, 1997b), South Korea (Kim, 2000), and Taiwan (Lin et al., 2005) all supported the finding that HSR systems encourage population migration to the served areas because of the increased accessibility and development planning of station areas. The experiences in Japan (Haynes, 1997a, 1997b), South Korea (Korea Transport Institute and Eastern Asia

Corresponding author. E-mail address: [email protected] (J.-J. Lin).

https://doi.org/10.1016/j.jtrangeo.2020.102662 Received 6 March 2019; Received in revised form 29 January 2020; Accepted 4 February 2020 0966-6923/ © 2020 Elsevier Ltd. All rights reserved.

Journal of Transport Geography 83 (2020) 102662

J.-J. Lin and Z.-X. Xie

discussed by a limited number of previous studies—are governmental policies and market forces. Governmental policies usually aim to encourage local developments, but they may also result in unintended negative effects on vulnerable groups. Curran and Hanson (2005), who studied the industrial displacement experiences of the Williamsburg neighborhood members in Brooklyn, New York, reported that as the “global city” label became a central aspect of New York City's identity, government leaders implemented policies that increasingly supported economic sectors whose activities were viewed as congruent with the global market label while undermining those sectoral activities that primarily served the local market. By conventional wisdom, these policies have created a sense that New York City is a difficult place to start and grow businesses for small-scale manufacturers. Lim et al. (2013) used the Cheonggye Stream Restoration Project in Seoul, South Korea as a case study to explore the impacts of a new large-scale open space within a city center on land uses in adjacent areas. They suggested that manufacturers dependent on low rents will be likely displaced by commercial users who are more capable of paying high rents when the surroundings and open spaces become increasingly improved and attractive. The displaced industries include metal work, printing, trophy and plaque manufacturing and other light manufacturing, and retail of manufactured goods, whereas the economic players that benefitted from the substituted land uses include office owners, educational institutes, commercial users, and hotel owners. This upgrading process, particularly when the original industry players with originally low rents are displaced by industry players capable of paying high rents, negatively affects the original workers of the displaced businesses. The market forces of industrial gentrification have been reported in two New York studies. Curran (2007) believed that urban manufacturers in gentrifying neighborhoods can be displaced because their spaces become attractive to developers, who then convert lofts into residences. The small manufacturers are regularly displaced by gentrifying residences through buyouts, lease refusals, zoning changes, and increased rents, thereby endangering the diversity of the economy and the employment outcomes of unskilled and immigrant workers. Yoon and Currid-Halkett (2015) performed survival analyses to compare the risks of closing among businesses in the gallery, art, and cultural industries that were established before the revitalization stage (early arrivers) and in the latter stage (latter-arrivers) within West Chelsea, New York versus the corresponding risks in their reference areas. The authors found that West Chelsea is an advantageous location overall for latter-arrivers in surviving in their market, whereas the early arrived gallery and individual artists' enterprises face a high risk of their operations closing. Moreover, a high proportion of new gallery and arts and cultural industries remain attracted to West Chelsea after 2000, suggesting that firms in those industries may be benefiting from the agglomeration effects and localization economies associated with colocation. The aforementioned driven forces are related to living or business environment improvements. Launching HSR services can improve intercity accessibility of station areas, and such an improvement benefits the surrounding business environment. Could the launching of HSR services be a considerably driving force of industrial gentrification? Two clues in the literature can be used as reference to hypothetically answer this question. The first clue is the argument of transit-induced gentrification based on the theoretical model of LeRoy and Sonstelie (1983). They applied the Alonso–Muth model (Alonso, 1964; Muth, 1969) to explain the influence of declining car costs on residential locations of the rich and the poor and argued that the affluent resided at the city center before the era when cars were used as commuting modes, moved to suburbs when cars became affordable for only the rich, and returned to city center as cars were affordable to the rich and the poor. Extending the model of LeRoy and Sonstelie (1983), Lin (2002) hypothesized that rail transit station access induces gentrification and empirically confirmed the hypothesis using the property value data in Chicago. Subsequently, numerous empirical evidence have been

Society of Transportation Studies, 2015), and Taiwan (Lin et al., 2005) suggest that HSR is positively related to the distribution of employment opportunities. Evidence from South Korea reveals that industry players near new station areas benefit from higher land price growths and better national averages than those near old station areas (Korea Transport Institute and Eastern Asia Society of Transportation Studies, 2015). Nakamura and Ueda (1989) found that the accessibility to Shinkansen in Japan increases land values in commercial areas by 67%. Evidence from Taiwan reveals that HSR impact on land prices varies with local real estate markets and station locations (Anderson et al., 2010). HSR services attract the migration of population and businesses to locations near station areas or those areas well-connected to stations. HSR services raise the land prices in these areas. Increased accessibility and land prices can result in the displacement of lower-rent-affordability activities by higher-rent-affordability activities, also known as “gentrification.” Industrial displacement occurs when originally existing industries near HSR station areas are displaced by newly moving-in industries. Industrial gentrification is likely to occur when the displaced industries are the lower-skill or lower-wage types (“L industries,” i.e., usually with lower land-rent-affordability) and the moving-in industries are the higher-skill or higher-wage types (“H industries,” i.e., mostly with higher land-rent-affordability) (Yoon and Currid-Halkett, 2015). This industry-upgrading process is expected to raise economic growth but can also endanger local economic diversity and employment outcomes of unskilled and immigrant workers (Curran, 2007). However, the existing literature has not well clarified the occurrence of industrial displacement in localities near HSR station areas and its association with gentrification. To resolve the aforementioned research gap in extant literature, this study aims to empirically determine the association of HSR with industrial displacement within localities near station areas and whether this industrial displacement is related to gentrification. The areas surrounding two HSR stations (i.e., Hangzhou Railway Station (HRS) in a developed urban area and HERS in a newly developing area in China), which launched HSR services earlier than the others (2010 and 2013, respectively), were selected as the empirical study areas. A total of 67,367 observations of business registration records for the HSR station area (test group) and non-HSR station area (comparison group) from 2010 to 2018 were collected as the study data. On the basis of the staff’ education and industry-related income levels, the study data (“observations”) were clustered into H and L industries. Then, the survival risks of businesses between H and L industries and between test and comparison groups were compared using survival analysis methods. The empirical results can provide novel evidence on the relationships between HSR stations and industrial gentrification that have not been mentioned in the literature and contribute knowledge on the relation of infrastructure investments to gentrification. 2. Industrial gentrification and rail infrastructures Gentrification, a concept proposed by Glass (1964), initially reflects the changing process of how the original working class residents of a city center in London were displaced by middle class residents who immigrated from suburban areas. Subsequently, gentrification has been globally explored by many researchers, and its meanings have been extended from social classes of residents to diverse issues, such as rural gentrification (Phillips, 1993), super gentrification (Lees, 2003), studentification and gentrification (Smith and Holt, 2007), commercial gentrification (Wang, 2011), and the multiple types of gentrification collected by Lees and Phillips (2018). Three common features of gentrification, namely, class upgrading, displacement, and negative impacts on vulnerable groups, have been reported in previous research. The displacement of originally existing L industries by newly moving-in H industries can exemplify the common features of gentrification. The two major driving forces of industrial displacement—although 2

Journal of Transport Geography 83 (2020) 102662

J.-J. Lin and Z.-X. Xie

Fig. 1. The station areas of HRS and HERS (red) and the comparison areas (yellow and green). (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

and living environment. However, accessibility improvement benefits business environment and land values and could result in displacements of lower-rent-affordability industries by higher-rent-affordability industries. Consequently, the first hypothesis to be tested in this research is as follows:

reported worldwide to support the associations of rail transit with gentrification, such as those from Canada (Grube-Cavers and Patterson, 2015), China (Zheng and Kahn, 2013), Italy (Pagliara and Papa, 2011), Taiwan (Lin and Chung, 2017; Lin and Yang, 2019), Thailand (Moore, 2015), and the USA (Feinstein and Allen, 2011; Kahn, 2007; Plevak, 2010; Pollack et al., 2010). The results of Padeiro et al. (2019)’s review suggested that proximity to transit may certainly contribute to gentrification. However, few studies, such as the Portland survey of Dong (2017), did not support the relationship. The second clue is the concept of spatial capital that is recognized as a connection between transport infrastructures and gentrification. The term “spatial capital” was proposed by a number of researchers using Pierre Bourdieu's social theory to interpret the engagement of people with place and space (Mace, 2017). Levy (2013) defined spatial capital as the set of resources, accumulated by an actor, enabling her/him to engage with place and space, to profit, in accordance with her/his strategies, using the spatial dimension of the society (Rérat, 2018). The survey results in Rérat and Lees (2011) argued that proximity and accessibility to city centers, walking/biking facilities, public transport services, commerce, services, and workplaces are the important determinants of new-build gentrifiers' residential choices, and such proximity and accessibility are a kind of spatial capital. Rérat (2018) suggested that the uneven impacts of new transport infrastructures on spatial capital endowment among classes lead to direct and indirect displacements. The direct displacement refers to the eviction of a population to build a transport infrastructure, such as the North Rail–South Rail Linkage Project in Manila, Philippines that led to the removal of informal settlement and the formalization of land uses (Choi, 2016). The indirect displacement is because of the increased level of accessibility that enlarges property values and increases displacement pressures, such as the previously mentioned transit-induced gentrification. The transit-induced gentrification and spatial capital concept support that new transport infrastructures could lead to residential gentrification in nearby areas because of the improvements in accessibility

H1. Newly launched HSR services induce industrial gentrification near station areas. Two types of HSR stations exist in practice, namely, previously existing rail stations (old stations) in developed urban areas and newly launched HSR stations (new stations) in suburban or urban periphery areas. A few previous research findings revealed that the socioeconomic impacts of rail transit infrastructures vary among different urban settings. The Hong Kong study of Loo et al. (2017) found that the impacts of rail transit on the developments near rail transit stations differ when it is built in greenfield sites as opposed to infill sites. The Taipei study of Lin and Chung (2017) confirmed that metro-induced gentrification differs between inner and outer city areas. A major reason for the aforementioned findings is the availability of developable lands for new developments in the greenfield sites and outer city areas. Industrial displacements around the new stations may be less prominent than those around the old stations because newly developing areas provide more available vacant lands than developed urban areas. Accordingly, the second hypothesis to be tested in this research is as follows: H2. The HSR-induced industrial gentrification in developed station areas is more significant than that in newly developing station areas. 3. Survival analysis 3.1. Data To test the two hypotheses, Hangzhou City was selected as the study city because it is one of the few cities that has operated old and new HSR stations for five years or more in China, and its cumulative HSR 3

Journal of Transport Geography 83 (2020) 102662

J.-J. Lin and Z.-X. Xie

and resident services, repairs, and other services. The other 10 categories were clustered as H industries as follows: elasticity, gas, and water production and supply; information transmission, software, and information technology services; financing; real estate; leasing and business services; scientific research and technical services; education; public health and social works; culture, sports, and entertainment; and public administration, social security, and social organization. The criteria-measured data were sourced from the China Labor Statistical Yearbook, and the industry categories were identified in accordance with the definitions of the Industrial Classification for National Economic Activities of the National Bureau of Statistics of China.

operations are sufficient for the requirements of this research. Chen and Wei (2013) studied the HERS, and their findings provided a reference for the discussions of this research. Hangzhou, the capital city of Zhejiang Province in eastern China, has 9.648 million residents within a land area of 16,853.57 km2 as of 2017. Fig. 1 illustrates the geographical features of the two study stations. HRS is an old station located at Shangcheng District (the city center of Hangzhou), and HERS is a new station located at Jianggan District (a newly developing urban area of the city). HSR services have been operated since October 26, 2010 for HRS and July 1, 2013 for HERS. The two selected station areas are marked in red in Fig. 1. The station area of HERS is marked in the Chengdong New Town Plan, which is a HERS-oriented urban planning project. The station area of HRS has a size similar to that of HERS. The two station areas are near the Primary Development Zone (Pol, 2003), which is approximately 5–10 min of travel time and directly affected by an HSR station. In addition to the station areas, this research selected administrative districts that host or are located near the station areas as the comparison areas. Shangcheng and Xiacheng Districts were identified as the comparison area of HRS (yellow area in Fig. 1), and Jianggan District was identified as the comparison area of HERS (green area in Fig. 1). The comparison areas were used to examine the differences of industrial changes associated with station areas and those areas located relatively far from HSR stations. This research successfully collected 61,343 effective observations as the study sample. Each observation was referenced to a business record, particularly, those that cite business locations within the station areas and the comparison areas, and with business operation durations covered by the period from the HSR's launching date to March 30, 2018 (i.e., the date when the study data were collected for this research). Table 1 lists the distribution of observation numbers of the areas and industries. The time-consuming data collection was conscientiously progressed to prepare the observations and their variable data. The records on business registrations and corresponding operation durations were obtained from the China Data Store (www.chinadatastore. com), and the selected variable data were separately verified using the National Enterprise Credit Information Publicity System (gsxt.gdgs.gov. cn), which is operated by the Chinese government. For each observation, the following attributes were collected to set the sample data: name, industry category, business type (government-owned or jointly owned, overseas investment, nongovernmental, or self-employed), address, registration date, and cancellation date of registration. With regard to differentiating L industries from H industries, this research applied a systematic clustering method using two criteria, namely, average yearly wage in 2009, 2012, and 2015 and average percentage of college-educated workers in 2009, 2012, and 2015. The criteria were measured at the national scale, and the clustering results were derived (Fig. 2). Among the 19 industry categories, nine industries were clustered as L industries as follows: farming, forestry animal husbandry, and fishery; mining and quarrying; manufacturing; construction; wholesale and retail trade, and catering services; transport, storage, post, and telecommunications; accommodation and catering; water conservancy, environment, and public facilities management;

3.2. Methods Table 2 lists the results of survival analyses. The top items in the table represents the tracking of each observation between Dates 1 (launching date of HSR services) and 2 (the most recent date before data collection) and the recording of survival days for an area. In the survival analyses, an “event” corresponds to an observation that is missing (i.e., a business has closed or moved out) from the collected data, whereas a survival observation indicates the absence of an event. This research considered four types of observations, namely, type-a observations for businesses that existed within the study areas before Date 1 and whose events before Date 2 have been recorded, type-b observations for businesses that existed within the study areas before Date 1 and survived until Date 2, type-c observations for businesses that appeared within the study areas after Date 1 and whose events before Date 2 have been recorded, and type-d observations for businesses that appeared within the study areas after Date 1 and survived until Date 2. Analyses 1 and 2 (Table 2) were used to test whether the survival days of H industries were longer than those of L industries within the HSR station areas. Furthermore, this research applied the Kaplan–Meier estimator to approximate the survival functions of H and L industry observations in HRS and HERS areas. The Kaplan–Meier estimator is a nonparametric statistical method that is widely applied in survival analyses. The estimator is given expressed as

S (t ) =

L H Total

HRS (Oct. 26, 2010-Mar. 30, 2018)

HERS (Jul. 1, 2013-Mar. 30, 2018)

Station area

Comparison area

Station area

Comparison area

2780 1443 4223

15,671 10,562 26,233

1855 905 2760

16,831 11,296 28,127

⎛1 − di ⎞ ni ⎠ ⎝ ⎜

⎟

(1)

where S (t ) is the cumulative survival rate at time t, ti is a time when at least one event has been recorded, di is the number of events that have occurred at time ti, and ni represents the individual entries known to have survived (or have yet to be recorded with an event or have yet to be censored) at time ti. The details of the estimators can be found in the work of Kaplan and Meier (1958). The average survival days of H and L industries can be estimated on the basis of the estimated survival functions. Log Rank test (Mantel, 1966), Breslow test (Breslow, 1970), and Tarone–Ware test (Tarone and Ware, 1977) were applied to examine whether the survival functions are significantly different between H and L industries. The L industries are considered to be displaced by H industries in the HSR station areas when the average survival days of L industries are shorter than those of H industries and the abovementioned tests support the significant difference of the survival functions between the two industries. Analyses 3–6 (Table 2) were used to test the difference in the survival risks of the studied observations in terms of the relationship of HSR station areas with the comparison areas. In particular, analyses 3 and 5 were performed to examine whether L industries in the HSR station areas have higher survival risks than those in the comparison areas, whereas analyses 4 and 6 were conducted to examine whether H industries in the HSR station areas have lower survival risks than those in the comparison areas. The occurrence of industrial gentrification in the HRS (or HERS) station area is considered to be associated with HSR services when the arguments tested by analyses 3 and 4 (or analyses 5 and 6) are supported by empirical data. This research applied the Cox

Table 1 Effective observation numbers. Industries

∏ i : ti ≤ t

Total

37,137 24,206 61,343

4

Journal of Transport Geography 83 (2020) 102662

J.-J. Lin and Z.-X. Xie

Fig. 2. Clusters of lower-skill or lower-wage and higher-skill or higher-wage industries. Table 2 Survival analyses in this research. Analyses

Observations

Date 1

Date 2

Tested arguments

1 2 3 4 5 6

In the HRS area In the HERS area L industries in HRS area and the comparison area H industries in HRS area and the comparison area L industries in HERS area and the comparison area H industries in HERS area and the comparison area

2010 2013 2010 2010 2013 2013

2018 2018 2018 2018 2018 2018

Survival days of H industries > that of L industries? Survival days of H industries > that of L industries? Risk in HRS area > risk in the comparison area Risk in HRS area < risk in the comparison area Risk in HERS area > risk in the comparison area Risk in HERS area < risk in the comparison area

Note. 2010 denotes October 26, 2010; 2013 denotes July 1, 2013; and, 2018 denotes March 30, 2018.

Fig. 3. Kaplan-Meier survival function curves.

proportional hazards model (Cox, 1972) to conduct analyses 3–6. The Cox model is a semiparametric model that does not assume a duration–time distribution and can conveniently model hazard functions when minimal or no knowledge of functional forms is available. The hazard function of the Cox model is specified as

rate) between times t and t + dt with a variable vector X, h0(t) is the baseline hazard rate, assuming that all elements of a variable vector X are zero, and β is a vector of the estimated parameters and used to determine the associations of variables with hazard rate. This research applied the following explanatory variables for X:

h (t , X ) = h 0 (t ) exp(βX )

(1) HSR station: an observation is located at the HRS/HERS station area (=1) or the comparison areas (=0); and

(2)

where h(t, X) is the probability that an event will occur (i.e., hazard 5

Journal of Transport Geography 83 (2020) 102662

J.-J. Lin and Z.-X. Xie

and lower risks, respectively, in the station areas than those in the comparison areas. Despite the coefficient of the L model being statistically insignificant at p = .1, the arguments of analyses 3 and 4 are supported by the empirical data to a certain extent. Consequently, the test results of analyses 1, 3, and 4 confirm that industrial gentrification occurs in the old station area and is correlated to the launching of HSR services. With regard to the HERS models, the L and H industries exhibit relatively lower risks in the station areas than in the comparison areas. Only the coefficient of the HSR station variable in the H model is consistent with the expectation of analysis 6, but the result is insignificant at p = .1. Combining the test results of analyses 2, 5, and 6 suggests that industrial gentrification has not occurred in the new station area. Conversely, the launching of HSR services appears to be beneficial to the survival of L industries in the new station area. This unexpected result is discussed in the subsequent section. The aforementioned results conditionally support the proposed hypotheses in this research. H1 (i.e., newly launched HSR services induce industrial gentrification in a developed station area) is only supported by the case of the old station area. Meanwhile, H2 is axiomatically supported because the occurrence of HSR-induced industrial gentrification has not been revealed for the newly developing station area.

Table 3 Testing for differences in survival days. Stations

Industries

Average survival days

Log Rank (MantelCox) tests

Breslow (Generalized Wilcoxon) tests

TaroneWare tests

HRS

L H L H

2448.049 2498.696 1645.148 1639.628

0.016⁎⁎

0.044⁎⁎

0.028⁎⁎

0.563

0.429

0.488

HERS

⁎⁎

Significant at p < .05.

Table 4 Cox proportional hazards regression models (event: an observation that is missing (i.e., a business has closed or moved out) from the collected data). Explanatory variable

Area Comparison HSR station Business type Government- or jointly owned Overseas investment Non-governmental Self-employed Likelihood ratio test (χ2) Number of observations

HRS

HERS

L model

H model

L model

H model

Coef.

Coef.

Coef.

Coef.

base 0.033

Base −0.223⁎⁎

base −0.202⁎⁎

base −0.087

base

base

base

base

0.275 −0.554⁎⁎ −0.385⁎ 63.904⁎⁎⁎ 18,449

0.448 0.027 −0.435 16.922⁎⁎⁎ 12,005

0.153 −0.549 −0.055 104.529⁎⁎⁎ 18,686

0.658 0.322 1.27⁎⁎ 45.297⁎⁎⁎ 12,201

4. Discussions The empirical evidence of this research supports the hypothesis on HSR-induced industrial gentrification in a developed station area and reveals two implications. The first implication is that launching a new rail transportation service should be considered a possible driving force of gentrification. The transit-induced gentrification studies reviewed in the second section have proven the associations of urban rail transit systems with residential gentrification. The results of this research emphasize that HSR (i.e., an upgrade of intercity rail system) is associated with industrial gentrification. In particular, improvements in intracity and intercity accessibilities by rail systems are likely related to the occurrence of gentrification in station areas. According to Hackworth and Smith (2001), state or governmental policies are the significant driving forces of gentrification. Thus, upgrading transportation infrastructures or services appears to be a notable governmental policy related to gentrification, but this assumption has not been widely explored in the past. In addition to the “global city” policies raised by Curran (2007) and the urban regeneration policy discussed by Lim et al. (2013), this research suggests a third policy-related reason of industrial gentrification, that is, transportation policies. The second implication is related to the implications of HSR impact on industrial development. The previous HSR impact studies have mostly focused on what industries are attracted by HSR station areas and the reasons behind the moving-in of these industries. According to the reviews of Albalate et al. (2012) and Haynes (1997a, 1997b), the availability of HSR services contributes to the attractiveness of a location for tertiary industries, especially among those involved in business services and financing and real estate services. With regard to industries benefitting from HSR operations, the launching of an HSR service implies a positive effect. However, the launching of HSR services creates a pressure of businesses closing down or leaving their original locations among industry players whose land-rent-affordability capabilities cannot compete with the more equipped industry players attracted by HSR. Part of the local labor faces employment issues, such as unemployment, inappropriate employment, and high commuting costs, when closing down or relocation occurs. These employment issues can result in the displacement of residents in the long term (Curran, 2007). Thus, the launching of HSR services implies positive and negative effects among different industries in developed urban areas. A pertinent concern of this research is knowing why the empirical evidence for the studied newly developing station area (i.e., HERS) did not support the hypothesized HSR-induced industrial gentrification. Three possible explanations are provided in this study to discuss the

⁎⁎⁎ ⁎⁎ ⁎

Significant p < .01. Significant at p < .05. Significant at p < .1.

(2) Business type: an observation is an overseas investment enterprise (Overseas investment = 1), a nongovernment-owned enterprise (Nongovernmental = 1), a self-employed business (Self-employed = 1), or a government-owned or jointly owned enterprise (Overseas investment = Nongovernmental = Self-employed = 0). The estimated parameters of the HSR station variable were used to examine how the empirical data support the test arguments of analyses 3–6. The variables on business type were used as the control to explain the hazard rates, which are based on the empirical findings of Sun et al. (2008) and Sun and Wang (2013) for China. 3.3. Results SPSS 22 was used to conduct the survival analyses. Fig. 3 illustrates the Kaplan–Meier survival function curves of the L and H industry observations in the HSR station areas. The findings show that the H industry observations have higher cumulative survival rates than that of the L industry observations for the old station area (i.e., HRS), but the two curves overlap for the new station area (i.e., HERS). The test results of analyses 1 and 2, which are listed in Table 3, are consistent with those in Fig. 3. The average survival days of H industries are significantly longer than those of L industries for the HRS area. By contrast, the average survival days differ between L and H industries for the HERS area. These results imply that the empirical data support the argument of analysis 1 rather than that of analysis 2. In other words, the industrial displacement of L industries by H industries occurs in the old station area rather than in the new station area. Table 4 lists the estimation results of the Cox models. The estimated coefficients of the HSR station variable represent the differences of hazard risk in the station areas relative to that in the comparison areas. With regard to the HRS models, the L and H industries exhibit higher 6

Journal of Transport Geography 83 (2020) 102662

J.-J. Lin and Z.-X. Xie

Fig. 4. The satellite images of the HERS area (Chengdong New Town) in 2004 (upper left), 2009 (upper right), 2013 (lower left) and 2017 (lower right). (Source: Google Earth)

results. The first explanation is related to the collected business registration records that cannot sufficiently explain the industrial displacement near HERS. Fig. 4 shows the satellite images of the HERS area, specifically the planning areas of Chengdong New Town in 2004, 2009, 2013, and 2017. The originally existing agricultural lands in 2004 have been gradually displaced by roads, buildings, facilities, and vacant lands to be used for development, implying that agricultural production activities are to be gradually displaced by housing and secondary or tertiary industries. This issue on displacement was mentioned by Chen and Wei (2013), who investigated the HERS area and found a significant transformation from rural to urban land utilization because of the launching of HSR services and the implementation of new town development projects. Considering that agricultural producers usually do not need to register their business (i.e., no business records), the displacement of agricultural production by tertiary industries cannot be determined using only the collected sample data. The two remaining explanations are the responses to the question on why L industries (except agricultural production) have experienced lower survival risks in the HERS area than that in the comparison area. With regard to the second explanation, the L industries and their locations in the station area appeared to have benefitted from the HSR services, and they subsequently acquired better operating conditions than those in the comparison area. Consequently, the improved revenues allowed the L industry players to afford the increased land rents, thereby enhancing their capability to compete with H industry players and allowing them to stay long in the station area. The third explanation can be attributed to vacant lands that have been made accessible to H industries in the newly developing station area. Therefore, L industries are less pressured by displacement risks that are to be likely caused by H industries. Around 37% of the station area of HERS (i.e., 345.8 ha among 931.6 ha) was undeveloped or agricultural lands in

2010.1 In short, L industries in the new station area have high competitiveness in terms of land-rent-affordability and experience low pressure in terms of displacement by H industries. The third explanation mirrors the rent gap theory originally proposed by Smith (1979) and extended by many researchers, such as Clark (1988). A rent gap denotes the disparity between potential and actual ground rent capitalized under present land use. This theory states that investing in the property market only occurs when a sufficient rent gap exists and induces displacements among different classes. The chances of rent gaprelated displacement to occur decrease in given areas where land is sufficiently available. The second and third explanations provide a unique feature of industrial gentrification in suburb HSR station areas in contrast with residential gentrification discussed in the literature. Hackworth and Smith (2001) believed that gentrification expands within inner city neighborhoods and to remote neighborhoods beyond the immediate city core, and this argument has been confirmed by numerous empirical evidence on the inner suburbs (e.g., Badcock, 2001; Rankin and McLean, 2015), suburbs (e.g., Collins, 2013; Niedt, 2006), and rural areas (e.g., Stockdale, 2010; Phillips, 1993). However, the argument is based on the context of residential activities conducted by normal neighborhoods. With regard to a newly developing HSR station area, a nonzero-sum competition exists in terms of available lands between L and H industries, and the two industries benefit from HSR services in terms of revenue earnings. Accordingly, HSR-induced industrial gentrification is insignificant within a suburb new station

1 Data of Chengdong New Town Plan from Bureau of Planning and Natural Resources, Hangzhou City (http://planning.hangzhou.gov.cn/ DesktopModules/GHJ.PlanningNotice/PlanningInfoGH.aspx?GUID= 20120223093521866)

7

Journal of Transport Geography 83 (2020) 102662

J.-J. Lin and Z.-X. Xie

area. The aforementioned discussions draw our attention to the issue on the development of an HSR station area with relatively low social impacts on the originally existing workers of L industries. After the launching of HSR services, the job opportunities of lower-rent-affordability businesses and agricultural production have gradually declined in old and new station areas. The social impacts of employment will manifest when the originally existing workers of L industries cannot cope with the change in their employment tracks. The impacts include unemployment (unable to find new jobs), long commuting distance (due to new work places), and inappropriate employment (changing jobs that do not fit with the workers' capabilities or expectations). Thus, policies that support locally owned businesses and adjust the employability of local workers should be considered in the development planning of HSR station areas.

inner suburbs, 1966-96. Urban Stud. 38 (9), 1559–1572. Breslow, N., 1970. A generalized Kruskal-Wallis test for comparing K samples subject to unequal patterns of censorship. Biometrika 57 (3), 579–594. Cao, J., Liu, X.C., Wang, Y., Li, Q., 2013. Accessibility impacts of China’s high-speed rail network. J. Transp. Geogr. 28, 12–21. Chen, Z., 2017. Impacts of high-speed rail on domestic air transportation in China. J. Transp. Geogr. 62, 184–192. Chen, Z., Haynes, K.E., 2017. Impact of high-speed rail on regional economic disparity in China. J. Transp. Geogr. 65, 80–91. Chen, C.L., Wei, B., 2013. High-speed rail and urban transformation in China: the case of Hangzhou East rail station. Built Environ. 39 (3), 385–398. Chen, Z., Xue, J., Rose, A.Z., Haynes, K.E., 2016. The impact of high-speed rail investment on economic and environmental change in China: a dynamic CGE analysis. Transp. Res. A Policy Pract. 92, 232–245. Cheng, Y., Loo, B.P.Y., Vickerman, R., 2015. High-speed rail networks, economic integration and regional specialization in China and Europe. Travel Behav. Soc. 2, 1–14. Choi, N., 2016. Metro Manila through the gentrification lens: disparities in urban planning and displacement risks. Urban Stud. 53 (3), 577–592. Clark, E., 1988. The rent gap and transformation of the built environment: case studies in Malmö 1860-1985. Geografiska Annaler. Series B, Human Geography 70 (2), 241–254. Collins, D., 2013. Gentrification or ‘multiplication of the suburbs’? Residential development in New Zealand’s coastal countryside. Environ Plan A 45, 109–125. Cox, D.R., 1972. Regression models and life-tables. J. R. Stat. Soc. Ser. B 34 (2), 187–220. Curran, W., 2007. From the frying pan to the oven: gentrification and the experience of industrial displacement in Williamsburg, Brooklyn. Urban Stud. 44 (8), 1427–1440. Curran, W., Hanson, S., 2005. Getting globalized: urban policy and industrial displacement in Williamsburg, Brooklyn. Urban Geogr. 26 (6), 461–482. Dong, H., 2017. Rail-transit-induced gentrification and the affordability paradox of TOD. J. Transp. Geogr. 63, 1–10. Feinstein, B.D., Allen, A., 2011. Community Benefits Agreements with Transit Agencies: Neighborhood Change along Boston’s Rail Lines and a Legal Strategy for Addressing Gentrification. 4(2). Social Science Electronic Publishing, pp. 204–209. Feng, C.M., Lin, J.J., Lai, Y.C., 2018. High speed railways in Asia. In: Zhang, J., Feng, C.M. (Eds.), Routledge Handbook of Transport in Asia. Routledge, New York, pp. 27–43. Fu, X., Zhang, A., Lei, Z., 2012. Will China’s airline industry survive the entry of highspeed rail? Res. Transp. Econ. 35, 13–25. Glass, R.L., 1964. Aspects of Change. Vol. 3 MacGibbon & Kee, London. Grube-Cavers, A., Patterson, Z., 2015. Urban rapid rail transit and gentrification in Canadian urban centres: A survival analysis approach. Urban Stud. 52 (1), 178–194. Hackworth, J., Smith, N., 2001. The changing state of gentrification. Tijdschr. Econ. Soc. Geogr. 92 (4), 464–477. Haynes, K.E., 1997a. Labor market and regional transportation improvements: the case of high-speed trains: An introduction and review. Annals of Regional Science 31, 51–76. Haynes, K.E., 1997b. Labor markets and regional transportation improvements: the case of high-speed trains: an introduction and review. Ann. Reg. Sci. 31, 57–76. Kahn, M.E., 2007. Gentrification trends in new transit-oriented communities: evidence from 14 cities that expanded and built rail transit systems. Real Estate Econ. 35 (2), 155–182. Kaplan, E.L., Meier, P., 1958. Nonparametric estimation from incomplete observations. Journal of the American Statistal Association 53 (282), 457–481. Kim, S.S., 2000. High-speed rail developments and spatial restructuring: a case study of the capital region in South Korea. Cities 17 (4), 251–262. Lees, L., 2003. Super-gentrification: the case of Brooklyn heights, New York city. Urban Stud. 40 (12), 2487–2509. Lees, L., Phillips, M., 2018. Handbook of Gentrification Studies. Edward Elgar Publishing, Cheltenham, UK. LeRoy, S.F., Sonstelie, J., 1983. Paradise lost and regained: transportation innovation, income, and residential location. Urban Economics 13 (1), 67–89. Levy, J., 2013. Capital spatial, in Dictionnaire de la geographie et de l’espace des societes. Belin, Paris, pp. 147–149. Lim, H., Kim, J., Potter, C., Bae, W., 2013. Urban regeneration and gentrification: land use impacts of the Cheonggye stream restoration project on the Seoul’s central business district. Habitat International 39, 192–200. Lin, J.J., 2002. Gentrification and transit in Northwest Chicago. Transp. Q. 56 (4), 175–191. Lin, J.J., Chung, J.C., 2017. Metro-induced gentrification: a 17-year experience in Taipei. Cities 67, 53–62. Lin, J.J., Yang, S.H., 2019. Proximity to metro stations and commercial gentrification. Transp. Policy 77, 79–89. Lin, J.J., Feng, C.M., Hwang, L.C., 2005. Impacts of Taiwan high speed rail system on local developments. Quarterly Journal of Transportation Planning 34 (3), 391–412 (in Chinese). Loo, B.P.Y., Cheng, A.H.T., Nichols, S.L., 2017. Transit-oriented development on greenfield versus infill sites: some lessons from Hong Kong. Landsc. Urban Plan. 167, 37–48. Mace, A., 2017. Spatial capital as a tool for planning practice. Plan. Theory 16 (2), 119–132. Mantel, N., 1966. Evaluation of survival data and two new rank order statistics arising in its consideration. Cancer Chemother. Rep. 50 (3), 163–170. Moore, R.D., 2015. Gentrification and displacement: the impacts of mass transit in Bangkok. Urban Policy Res. 33 (4), 472–489. Muth, R.F., 1969. Cities and Housing: The Spatial Patterns of Urban Residential Land Use. University of Chicago Press, Chicago. Nakamura, H., Ueda, T., 1989. The impacts of the Shinkansen on regional development.

5. Conclusions On the basis of the empirical findings, the present research suggests that newly launched HSR services induce industrial gentrification in the developed station area. Except for the displacement of agricultural production, HSR-induced industrial gentrification has not occurred in the newly developing station area. The latter phenomenon is because of the sufficient lands made available for industrial development and the L and H industries that benefit from HSR services in terms of revenues. To the best of our knowledge, this evidence does not exist in the extant literature. Therefore, the finding of this research can broaden our understanding of the relation of gentrification to transportation infrastructure and newly created urban land. The empirical results can serve as reference for local administrations when implementing intervention measures to prevent the negative social impacts of HSR-induced industrial gentrification on locally pre-existing businesses and workers. Future studies on the relationships between HSR service launching and industrial gentrification of localities near stations should examine the three issues that reflect the limitations of this research. First, the empirical evidence of this research is based on a provincial capital city that hosts approximately 10 million residents, operates multiple HSR stations, and is located in an economically emerging state. Cities whose context is the same as Hongzhou can refer to the aforementioned arguments for local development policies in response to HSR schemes. Subsequent empirical studies should be conducted for cities with different contexts to clarify whether the arguments are universally applicable. Second, the differences between L and H industries in this research are based on the first-level categories of the official classification of Chinese industries. A total of 19 primary categories may be considered simplistic in determining the possible differences among the subcategories or lower-level subcategories. Future studies can differentiate L and H industries based on highly detailed industry categories when data are available. This research explored inter-industrial displacements rather than intra-industrial displacements. The former approach, similar to the research on Seoul by Lim et al. (2013), explores how a group of industries can be displaced by another group. The latter approach, similar to the research on New York by Yoon and CurridHalkett (2015), investigates how the early arrivers of the gallery, arts and cultural industries were displaced by late arrivers from the same industries. The association of HSR with intra-industrial gentrification should be explored in future studies. References Albalate, D., Bel, G., Tomer, A., 2012. High-speed rail: lessons for policy makers from experiences abroad. Public Adm. Rev. 72 (3), 336–350. Alonso, W., 1964. Location and Land Use. Harvard University Press, Cambridge, MA. Anderson, D.E., Shyr, O.F., Fu, J., 2010. Does high-speed rail accessibility influence residential property prices? Hedonic estimates from southern Taiwan. J. Transp. Geogr. 18 (1), 166–174. Badcock, B., 2001. Thirty years on: gentrification and class changeover in Adelaide’s

8

Journal of Transport Geography 83 (2020) 102662

J.-J. Lin and Z.-X. Xie

39 (1), 142–161. Stockdale, A., 2010. The diverse geographies of rural gentrification in Scotland. J. Rural. Stud. 26 (1), 31–40. Sun, X., Wang, J., 2013. Ownership and firm innovation efficiency. Chinese Journal of Management 10 (7), 1041–1047 (in Chinese). Sun, R., Wang, N.J., Shi, J.T., 2008. Innovation atmosphere differences among different enterprise types under Chinese context. Nankai Business Review 11 (2), 42–49 (in Chinese). Tarone, R.E., Ware, J., 1977. On distribution-free tests for equality of survival distributions. Biometrika 64, 156–160. The Korea Transport Institute and Eastern Asia Society of Transportation Studies, 2015. International Comparison on High-Speed Railway Station Area Development: Japan. The Korea Transport Institute, Taiwan and Korea, Seoul. UIC, 2018. High Speed Rail Brochure 2018. UIC, Paris. Wang, S.W., 2011. Commercial gentrification and entrepreneurial governance in Shanghai: a case study of Taikang road creative cluster. Urban Policy Res. 29 (4), 363–380. Wang, L., Liu, Y., Sun, C., Liu, Y., 2016. Accessibility impact of the present and future high-speed rail network: a case study of Jiangsu Province, China. J. Transp. Geogr. 54, 161–172. Yang, H., Zhang, A., 2012. Effects of high-speed rail and air transport competition on prices, profits and welfare. Transportation Research Part B: Methodology 46, 1322–1333. Yoon, H., Currid-Halkett, E., 2015. Industrial gentrification in West Chelsea, New York: who survived and who did not? Empirical evidence from discrete-time survival analysis. Urban Stud. 52 (1), 20–49. Zhang, W., Nian, P., Lyu, G., 2016. A multimodal approach to assessing accessibility of a high-speed railway station. J. Transp. Geogr. 54, 91–101. Zhang, Q., Yang, H., Wang, Q., 2017. Impact of high-speed rail on China’s big three airlines. Transp. Res. A Policy Pract. 98, 77–85. Zheng, S., Kahn, M.E., 2013. Does government investment in local public goods spur gentrification? Evidence from Beijing. Real Estate Econ. 41 (1), 1–28.

In: Paper Presented at the the Fifth World Conference on Transport Research, Yokohama. Niedt, C., 2006. Gentrification and the grassroots: popular support in the revanchist suburb. J. Urban Aff. 28 (2), 99–120. Padeiro, M., Louro, A., da Costa, N.M., 2019. Transit-oriented development and gentrification: a systematic review. Transp. Rev. 39 (6), 733–754. Pagliara, F., Papa, E., 2011. Urban rail systems investments: an analysis of the impacts on property values and residents’ location. J. Transp. Geogr. 19 (2), 200–211. Phillips, M., 1993. Rural gentrification and the processes of class colonization. J. Rural. Stud. 9 (2), 123–140. Plevak, S.H., 2010. The Impact of Light Rail Transportation Announcement and Construction: The Role of Rail Transit in Property Values, Land Use, Demographics, Equity, Accessibility, and Gentrification. Master Degree Thesis. University of Texas at Austin. Pol, P.M.J., 2003. The economic impact of the high-speed train on urban regions. In: The 43rd Congress of the European Regional Science Association, Jyväskylä, Finland. Pollack, S., Bluestone, B., Billingham, C., 2010. Maintaining diversity in America's transitrich neighborhoods: tools for equitable neighborhood change. In: Dukakis Certer for Urban and Regional Policy at Northeastern University, Boston. Rankin, K.N., McLean, H., 2015. Governing the commercial streets of the city: new terrains of disinvestment and gentrification in Toronto’s inner suburbs. Antipode 47 (1), 216–239. Rérat, P., 2018. Spatial capital and planetary gentrification: Residential location, mobility and social inequalities. In: Lees, L., Phillips, M. (Eds.), Handbook of Gentrification Studies. Edward Elgar Publishing, Cheltenham, UK, pp. 103–118. Rérat, P., Lees, L., 2011. Spatial capital, gentrification and mobility: evidence from Swiss core cities. Transactions of the Institute of British Geographers, NS 36, 126–142. Shaw, S.L., Fang, Z., Lu, S., Tao, R., 2014. Impacts of high speed rail on railroad network accessibility in China. J. Transp. Geogr. 40, 112–122. Smith, N., 1979. Toward a theory of gentrification: a back to the city movement by capital, not people. J. Am. Plan. Assoc. 45 (4), 538–548. Smith, D.P., Holt, L., 2007. Studentification and ‘apprentice’gentrifiers within Britain’s provincial towns and cities: extending the meaning of gentrification. Environ Plan A

9