Impacts of Urban Rail Transit on City Growth: Evidence from China View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2022-06-20

AUTHORS

Zhibin Tao, Xuesong Feng, Kemeng Li, Ruolin Shi

ABSTRACT

This research examines the effects of urban rail transit (URT) on city growth measured by the increases in population, gross domestic product (GDP) and employment rate. Forty cities which have URT systems by the end of 2019 in China are taken as investigated samples. Research data related to URT extent, population, GDP, employment rate and five types of control variables which are individual, people's living, economic, science and education, and infrastructure are utilized and their applicability is verified. Panel data models are applied to analyze the effect of URT on city growth, and the robustness of the model estimation results is assessed. The study further analyzes the heterogeneity in the effects of URT systems on cities with different economic development levels. The estimated results indicate that the opening and expansion of URT have a positive effect on the population of the city. URT promotes the development of the urban economy and increases employment opportunities. Nevertheless, because of population migration, URT has little effect on the employment rate. In addition, the positive effect of URT on urban growth is most obvious for cities with a relatively high level of economic development. More... »

PAGES

121-133

References to SciGraph publications

  • 2017-10-20. Spatial and time effect of subway on property prices in JOURNAL OF HOUSING AND THE BUILT ENVIRONMENT
  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/s40864-022-00169-8

    DOI

    http://dx.doi.org/10.1007/s40864-022-00169-8

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1148809873


    Indexing Status Check whether this publication has been indexed by Scopus and Web Of Science using the SN Indexing Status Tool
    Incoming Citations Browse incoming citations for this publication using opencitations.net

    JSON-LD is the canonical representation for SciGraph data.

    TIP: You can open this SciGraph record using an external JSON-LD service: JSON-LD Playground Google SDTT

    [
      {
        "@context": "https://springernature.github.io/scigraph/jsonld/sgcontext.json", 
        "about": [
          {
            "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/09", 
            "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
            "name": "Engineering", 
            "type": "DefinedTerm"
          }, 
          {
            "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/12", 
            "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
            "name": "Built Environment and Design", 
            "type": "DefinedTerm"
          }, 
          {
            "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/15", 
            "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
            "name": "Commerce, Management, Tourism and Services", 
            "type": "DefinedTerm"
          }, 
          {
            "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/0905", 
            "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
            "name": "Civil Engineering", 
            "type": "DefinedTerm"
          }, 
          {
            "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/1205", 
            "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
            "name": "Urban and Regional Planning", 
            "type": "DefinedTerm"
          }, 
          {
            "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/1507", 
            "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
            "name": "Transportation and Freight Services", 
            "type": "DefinedTerm"
          }
        ], 
        "author": [
          {
            "affiliation": {
              "alternateName": "School of traffic and transportation, Beijing Jiaotong University, Haidian District, No. 3 Shangyuancun, 100044, Beijing, People\u2019s Republic of China", 
              "id": "http://www.grid.ac/institutes/grid.181531.f", 
              "name": [
                "School of traffic and transportation, Beijing Jiaotong University, Haidian District, No. 3 Shangyuancun, 100044, Beijing, People\u2019s Republic of China"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Tao", 
            "givenName": "Zhibin", 
            "id": "sg:person.013602056252.57", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.013602056252.57"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "School of traffic and transportation, Beijing Jiaotong University, Haidian District, No. 3 Shangyuancun, 100044, Beijing, People\u2019s Republic of China", 
              "id": "http://www.grid.ac/institutes/grid.181531.f", 
              "name": [
                "School of traffic and transportation, Beijing Jiaotong University, Haidian District, No. 3 Shangyuancun, 100044, Beijing, People\u2019s Republic of China"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Feng", 
            "givenName": "Xuesong", 
            "id": "sg:person.012207115252.11", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.012207115252.11"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "School of traffic and transportation, Beijing Jiaotong University, Haidian District, No. 3 Shangyuancun, 100044, Beijing, People\u2019s Republic of China", 
              "id": "http://www.grid.ac/institutes/grid.181531.f", 
              "name": [
                "School of traffic and transportation, Beijing Jiaotong University, Haidian District, No. 3 Shangyuancun, 100044, Beijing, People\u2019s Republic of China"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Li", 
            "givenName": "Kemeng", 
            "id": "sg:person.013004475652.83", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.013004475652.83"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "School of traffic and transportation, Beijing Jiaotong University, Haidian District, No. 3 Shangyuancun, 100044, Beijing, People\u2019s Republic of China", 
              "id": "http://www.grid.ac/institutes/grid.181531.f", 
              "name": [
                "School of traffic and transportation, Beijing Jiaotong University, Haidian District, No. 3 Shangyuancun, 100044, Beijing, People\u2019s Republic of China"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Shi", 
            "givenName": "Ruolin", 
            "id": "sg:person.011411534652.93", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.011411534652.93"
            ], 
            "type": "Person"
          }
        ], 
        "citation": [
          {
            "id": "sg:pub.10.1007/s10901-017-9569-y", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1092292087", 
              "https://doi.org/10.1007/s10901-017-9569-y"
            ], 
            "type": "CreativeWork"
          }
        ], 
        "datePublished": "2022-06-20", 
        "datePublishedReg": "2022-06-20", 
        "description": "This research examines the effects of urban rail transit (URT) on city growth measured by the increases in population, gross domestic product (GDP) and employment rate. Forty cities which have URT systems by the end of 2019 in China are taken as investigated samples.\u00a0Research data related to URT extent, population, GDP, employment rate and five types of control variables which are individual, people's living, economic, science and education, and infrastructure\u00a0are utilized and their applicability is verified. Panel data models are applied to analyze the effect of URT on city growth, and the robustness of the model estimation results is assessed. The study further analyzes the heterogeneity in the effects of URT systems on cities with different economic development levels. The estimated results indicate that the opening and expansion of URT have a positive effect on the\u00a0population\u00a0of the city. URT promotes the development of the urban economy and increases employment opportunities. Nevertheless, because of population migration, URT has little effect on the employment rate. In addition, the positive effect of URT on urban growth is most obvious for cities with a relatively high level of economic development.", 
        "genre": "article", 
        "id": "sg:pub.10.1007/s40864-022-00169-8", 
        "isAccessibleForFree": true, 
        "isPartOf": [
          {
            "id": "sg:journal.1136445", 
            "issn": [
              "2199-6687", 
              "2199-6679"
            ], 
            "name": "Urban Rail Transit", 
            "publisher": "Springer Nature", 
            "type": "Periodical"
          }, 
          {
            "issueNumber": "2", 
            "type": "PublicationIssue"
          }, 
          {
            "type": "PublicationVolume", 
            "volumeNumber": "8"
          }
        ], 
        "keywords": [
          "gross domestic product", 
          "employment rate", 
          "different economic development levels", 
          "city growth", 
          "panel data model", 
          "economic development level", 
          "model estimation results", 
          "positive effect", 
          "domestic product", 
          "economic development", 
          "estimation results", 
          "urban economy", 
          "development level", 
          "employment opportunities", 
          "control variables", 
          "data model", 
          "urban growth", 
          "population migration", 
          "rail transit", 
          "urban rail transit", 
          "growth", 
          "economy", 
          "China", 
          "city", 
          "people's living", 
          "living", 
          "impact", 
          "heterogeneity", 
          "variables", 
          "URT system", 
          "infrastructure", 
          "high levels", 
          "evidence", 
          "opportunities", 
          "rate", 
          "research data", 
          "effect", 
          "model", 
          "development", 
          "education", 
          "levels", 
          "expansion", 
          "extent", 
          "products", 
          "results", 
          "transit", 
          "research", 
          "data", 
          "population", 
          "robustness", 
          "increase", 
          "little effect", 
          "opening", 
          "science", 
          "system", 
          "migration", 
          "end", 
          "types", 
          "study", 
          "samples", 
          "applicability", 
          "addition"
        ], 
        "name": "Impacts of Urban Rail Transit on City Growth: Evidence from China", 
        "pagination": "121-133", 
        "productId": [
          {
            "name": "dimensions_id", 
            "type": "PropertyValue", 
            "value": [
              "pub.1148809873"
            ]
          }, 
          {
            "name": "doi", 
            "type": "PropertyValue", 
            "value": [
              "10.1007/s40864-022-00169-8"
            ]
          }
        ], 
        "sameAs": [
          "https://doi.org/10.1007/s40864-022-00169-8", 
          "https://app.dimensions.ai/details/publication/pub.1148809873"
        ], 
        "sdDataset": "articles", 
        "sdDatePublished": "2022-10-01T06:49", 
        "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
        "sdPublisher": {
          "name": "Springer Nature - SN SciGraph project", 
          "type": "Organization"
        }, 
        "sdSource": "s3://com-springernature-scigraph/baseset/20221001/entities/gbq_results/article/article_927.jsonl", 
        "type": "ScholarlyArticle", 
        "url": "https://doi.org/10.1007/s40864-022-00169-8"
      }
    ]
     

    Download the RDF metadata as:  json-ld nt turtle xml License info

    HOW TO GET THIS DATA PROGRAMMATICALLY:

    JSON-LD is a popular format for linked data which is fully compatible with JSON.

    curl -H 'Accept: application/ld+json' 'https://scigraph.springernature.com/pub.10.1007/s40864-022-00169-8'

    N-Triples is a line-based linked data format ideal for batch operations.

    curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1007/s40864-022-00169-8'

    Turtle is a human-readable linked data format.

    curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/s40864-022-00169-8'

    RDF/XML is a standard XML format for linked data.

    curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/s40864-022-00169-8'


     

    This table displays all metadata directly associated to this object as RDF triples.

    160 TRIPLES      21 PREDICATES      91 URIs      78 LITERALS      6 BLANK NODES

    Subject Predicate Object
    1 sg:pub.10.1007/s40864-022-00169-8 schema:about anzsrc-for:09
    2 anzsrc-for:0905
    3 anzsrc-for:12
    4 anzsrc-for:1205
    5 anzsrc-for:15
    6 anzsrc-for:1507
    7 schema:author N33320ed0229b45fd9c4dad23f02934db
    8 schema:citation sg:pub.10.1007/s10901-017-9569-y
    9 schema:datePublished 2022-06-20
    10 schema:datePublishedReg 2022-06-20
    11 schema:description This research examines the effects of urban rail transit (URT) on city growth measured by the increases in population, gross domestic product (GDP) and employment rate. Forty cities which have URT systems by the end of 2019 in China are taken as investigated samples. Research data related to URT extent, population, GDP, employment rate and five types of control variables which are individual, people's living, economic, science and education, and infrastructure are utilized and their applicability is verified. Panel data models are applied to analyze the effect of URT on city growth, and the robustness of the model estimation results is assessed. The study further analyzes the heterogeneity in the effects of URT systems on cities with different economic development levels. The estimated results indicate that the opening and expansion of URT have a positive effect on the population of the city. URT promotes the development of the urban economy and increases employment opportunities. Nevertheless, because of population migration, URT has little effect on the employment rate. In addition, the positive effect of URT on urban growth is most obvious for cities with a relatively high level of economic development.
    12 schema:genre article
    13 schema:isAccessibleForFree true
    14 schema:isPartOf Naa95ba098bfe498c83397225df78ebf9
    15 Nad0cc5b7c7734757a1d61431d94a973c
    16 sg:journal.1136445
    17 schema:keywords China
    18 URT system
    19 addition
    20 applicability
    21 city
    22 city growth
    23 control variables
    24 data
    25 data model
    26 development
    27 development level
    28 different economic development levels
    29 domestic product
    30 economic development
    31 economic development level
    32 economy
    33 education
    34 effect
    35 employment opportunities
    36 employment rate
    37 end
    38 estimation results
    39 evidence
    40 expansion
    41 extent
    42 gross domestic product
    43 growth
    44 heterogeneity
    45 high levels
    46 impact
    47 increase
    48 infrastructure
    49 levels
    50 little effect
    51 living
    52 migration
    53 model
    54 model estimation results
    55 opening
    56 opportunities
    57 panel data model
    58 people's living
    59 population
    60 population migration
    61 positive effect
    62 products
    63 rail transit
    64 rate
    65 research
    66 research data
    67 results
    68 robustness
    69 samples
    70 science
    71 study
    72 system
    73 transit
    74 types
    75 urban economy
    76 urban growth
    77 urban rail transit
    78 variables
    79 schema:name Impacts of Urban Rail Transit on City Growth: Evidence from China
    80 schema:pagination 121-133
    81 schema:productId N94489648dc6d4d1c8ec671da69454356
    82 Na11b5fc2cc5f47938bcab43e57dc14b1
    83 schema:sameAs https://app.dimensions.ai/details/publication/pub.1148809873
    84 https://doi.org/10.1007/s40864-022-00169-8
    85 schema:sdDatePublished 2022-10-01T06:49
    86 schema:sdLicense https://scigraph.springernature.com/explorer/license/
    87 schema:sdPublisher N154bdeb7e3b64bbc801c4cb6ead837ca
    88 schema:url https://doi.org/10.1007/s40864-022-00169-8
    89 sgo:license sg:explorer/license/
    90 sgo:sdDataset articles
    91 rdf:type schema:ScholarlyArticle
    92 N154bdeb7e3b64bbc801c4cb6ead837ca schema:name Springer Nature - SN SciGraph project
    93 rdf:type schema:Organization
    94 N25dbf099e41647eda8e1375f3cce9c1d rdf:first sg:person.012207115252.11
    95 rdf:rest Ndbf0f3c001e6455785cd2f8d931b5171
    96 N33320ed0229b45fd9c4dad23f02934db rdf:first sg:person.013602056252.57
    97 rdf:rest N25dbf099e41647eda8e1375f3cce9c1d
    98 N94489648dc6d4d1c8ec671da69454356 schema:name doi
    99 schema:value 10.1007/s40864-022-00169-8
    100 rdf:type schema:PropertyValue
    101 Na11b5fc2cc5f47938bcab43e57dc14b1 schema:name dimensions_id
    102 schema:value pub.1148809873
    103 rdf:type schema:PropertyValue
    104 Naa95ba098bfe498c83397225df78ebf9 schema:volumeNumber 8
    105 rdf:type schema:PublicationVolume
    106 Nad0cc5b7c7734757a1d61431d94a973c schema:issueNumber 2
    107 rdf:type schema:PublicationIssue
    108 Nb50c538e11d6493e9c5a865fa97479d2 rdf:first sg:person.011411534652.93
    109 rdf:rest rdf:nil
    110 Ndbf0f3c001e6455785cd2f8d931b5171 rdf:first sg:person.013004475652.83
    111 rdf:rest Nb50c538e11d6493e9c5a865fa97479d2
    112 anzsrc-for:09 schema:inDefinedTermSet anzsrc-for:
    113 schema:name Engineering
    114 rdf:type schema:DefinedTerm
    115 anzsrc-for:0905 schema:inDefinedTermSet anzsrc-for:
    116 schema:name Civil Engineering
    117 rdf:type schema:DefinedTerm
    118 anzsrc-for:12 schema:inDefinedTermSet anzsrc-for:
    119 schema:name Built Environment and Design
    120 rdf:type schema:DefinedTerm
    121 anzsrc-for:1205 schema:inDefinedTermSet anzsrc-for:
    122 schema:name Urban and Regional Planning
    123 rdf:type schema:DefinedTerm
    124 anzsrc-for:15 schema:inDefinedTermSet anzsrc-for:
    125 schema:name Commerce, Management, Tourism and Services
    126 rdf:type schema:DefinedTerm
    127 anzsrc-for:1507 schema:inDefinedTermSet anzsrc-for:
    128 schema:name Transportation and Freight Services
    129 rdf:type schema:DefinedTerm
    130 sg:journal.1136445 schema:issn 2199-6679
    131 2199-6687
    132 schema:name Urban Rail Transit
    133 schema:publisher Springer Nature
    134 rdf:type schema:Periodical
    135 sg:person.011411534652.93 schema:affiliation grid-institutes:grid.181531.f
    136 schema:familyName Shi
    137 schema:givenName Ruolin
    138 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.011411534652.93
    139 rdf:type schema:Person
    140 sg:person.012207115252.11 schema:affiliation grid-institutes:grid.181531.f
    141 schema:familyName Feng
    142 schema:givenName Xuesong
    143 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.012207115252.11
    144 rdf:type schema:Person
    145 sg:person.013004475652.83 schema:affiliation grid-institutes:grid.181531.f
    146 schema:familyName Li
    147 schema:givenName Kemeng
    148 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.013004475652.83
    149 rdf:type schema:Person
    150 sg:person.013602056252.57 schema:affiliation grid-institutes:grid.181531.f
    151 schema:familyName Tao
    152 schema:givenName Zhibin
    153 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.013602056252.57
    154 rdf:type schema:Person
    155 sg:pub.10.1007/s10901-017-9569-y schema:sameAs https://app.dimensions.ai/details/publication/pub.1092292087
    156 https://doi.org/10.1007/s10901-017-9569-y
    157 rdf:type schema:CreativeWork
    158 grid-institutes:grid.181531.f schema:alternateName School of traffic and transportation, Beijing Jiaotong University, Haidian District, No. 3 Shangyuancun, 100044, Beijing, People’s Republic of China
    159 schema:name School of traffic and transportation, Beijing Jiaotong University, Haidian District, No. 3 Shangyuancun, 100044, Beijing, People’s Republic of China
    160 rdf:type schema:Organization
     




    Preview window. Press ESC to close (or click here)


    ...