IDEAS home Printed from https://ideas.repec.org/a/gam/jlands/v11y2022i7p1059-d861249.html
   My bibliography  Save this article

An Evaluation of the Development Performance of Small County Towns and Its Influencing Factors: A Case Study of Small Towns in Jiangyin City in the Yangtze River Delta, China

Author

Listed:
  • Xiao Gong

    (School of Geographical Sciences, Nanjing Normal University, Nanjing 210046, China)

  • Xiaolin Zhang

    (School of Geographical Sciences, Nanjing Normal University, Nanjing 210046, China
    Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing 210046, China)

  • Jieyi Tao

    (School of Geographical Sciences, Nanjing Normal University, Nanjing 210046, China)

  • Hongbo Li

    (School of Geographical Sciences, Nanjing Normal University, Nanjing 210046, China
    Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing 210046, China)

  • Yunrui Zhang

    (School of Architecture and Urban Planning, Nanjing University, Nanjing 210093, China)

Abstract

Research on the development performance of small towns is critical for promoting their revitalization, advancing urbanization, and high-quality development and transformation for realizing urban–rural integration. We used the DPSIR-DEA model to study the spatiotemporal evolution process and characteristics of the development performance of 14 small towns within the administrative division of Jiangyin city from 2001 to 2019. We subsequently applied a geographical detector model to analyze the spatiotemporal heterogeneity of the factors influencing the development performance of small towns. The results showed that 2012 was a turning point in the overall development performance index of small towns in Jiangyin, revealing initially decreasing and then increasing trends. The development performance index values of different types of small towns evidenced three trends: a steady increase, a continuous decrease, and an initial decrease followed by an increase. During 2001–2019, the development performance of Jiangyin’s small towns reflected a spatial evolution pattern of complete dispersion → small agglomeration → large agglomeration. An optimal spatial pattern comprised an increase in the number of towns demonstrating a high development performance and a decrease in the number of towns with a low development performance. GDP per capita, industrial investments, and construction land density were key influencing factors of development performance, which was mainly driven by economic and social factors, with ecological factors having a relatively weak influence.

Suggested Citation

  • Xiao Gong & Xiaolin Zhang & Jieyi Tao & Hongbo Li & Yunrui Zhang, 2022. "An Evaluation of the Development Performance of Small County Towns and Its Influencing Factors: A Case Study of Small Towns in Jiangyin City in the Yangtze River Delta, China," Land, MDPI, vol. 11(7), pages 1-22, July.
    • Handle: RePEc:gam:jlands:v:11:y:2022:i:7:p:1059-:d:861249
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2073-445X/11/7/1059/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2073-445X/11/7/1059/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Tony Binns & Etienne Nel, 2003. "The Village in a Game Park: Local Response to the Demise of Coal Mining in KwaZulu-Natal, South Africa," Economic Geography, Taylor & Francis Journals, vol. 79(1), pages 41-66, January.
    2. Malcolm Prowle & Manj Kalar & Lynne Barrow, 2016. "New development: Value for money (VFM) in public services—the importance of organizational culture," Public Money & Management, Taylor & Francis Journals, vol. 36(7), pages 547-552, November.
    3. Ehara, Makoto & Hyakumura, Kimihiko & Sato, Ren'ya & Kurosawa, Kiyoshi & Araya, Kunio & Sokh, Heng & Kohsaka, Ryo, 2018. "Addressing Maladaptive Coping Strategies of Local Communities to Changes in Ecosystem Service Provisions Using the DPSIR Framework," Ecological Economics, Elsevier, vol. 149(C), pages 226-238.
    4. Yang, Chunyu & Huang, Jue & Lin, Zhibin & Zhang, Danxia & Zhu, Ying & Xu, Xinghua & Chen, Mei, 2018. "Evaluating the symbiosis status of tourist towns: The case of Guizhou Province, China," Annals of Tourism Research, Elsevier, vol. 72(C), pages 109-125.
    5. Goto, Mika & Otsuka, Akihiro & Sueyoshi, Toshiyuki, 2014. "DEA (Data Envelopment Analysis) assessment of operational and environmental efficiencies on Japanese regional industries," Energy, Elsevier, vol. 66(C), pages 535-549.
    6. Denis Lawrence & John Houghton & Anna George, 1997. "International Comparisons of Australia's Infrastructure Performance," Journal of Productivity Analysis, Springer, vol. 8(4), pages 361-378, November.
    7. Hu, Xiaohui & Wu, Qianbo & Xu, Wei & Li, Yuwen, 2022. "Specialty towns in China: Towards a typological policy approach," Land Use Policy, Elsevier, vol. 114(C).
    8. Zhang, Zhengfeng & Liu, Jing & Gu, Xiaokun, 2019. "Reduction of industrial land beyond Urban Development Boundary in Shanghai: Differences in policy responses and impact on towns and villages," Land Use Policy, Elsevier, vol. 82(C), pages 620-630.
    9. Yin, Xu & Wang, Jing & Li, Yurui & Feng, Zhiming & Wang, Qianyi, 2021. "Are small towns really inefficient? A data envelopment analysis of sampled towns in Jiangsu province, China," Land Use Policy, Elsevier, vol. 109(C).
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Yin, Xu & Wang, Jing & Li, Yurui & Feng, Zhiming & Wang, Qianyi, 2021. "Are small towns really inefficient? A data envelopment analysis of sampled towns in Jiangsu province, China," Land Use Policy, Elsevier, vol. 109(C).
    2. Jianglin Lu & Keqiang Wang & Hongmei Liu, 2022. "Residents’ Selection Behavior of Compensation Schemes for Construction Land Reduction: Empirical Evidence from Questionnaires in Shanghai, China," Land, MDPI, vol. 12(1), pages 1-29, December.
    3. Sueyoshi, Toshiyuki & Goto, Mika, 2015. "Environmental assessment on coal-fired power plants in U.S. north-east region by DEA non-radial measurement," Energy Economics, Elsevier, vol. 50(C), pages 125-139.
    4. Sueyoshi, Toshiyuki & Goto, Mika, 2014. "Investment strategy for sustainable society by development of regional economies and prevention of industrial pollutions in Japanese manufacturing sectors," Energy Economics, Elsevier, vol. 42(C), pages 299-312.
    5. Geng, ZhiQiang & Dong, JunGen & Han, YongMing & Zhu, QunXiong, 2017. "Energy and environment efficiency analysis based on an improved environment DEA cross-model: Case study of complex chemical processes," Applied Energy, Elsevier, vol. 205(C), pages 465-476.
    6. Wang, Keqiang & Li, Guoxiang & Liu, Hongmei, 2021. "Porter effect test for construction land reduction," Land Use Policy, Elsevier, vol. 103(C).
    7. Dai, Bing & Gu, Xiaokun & Xie, Boming, 2020. "Policy Framework and Mechanism of Life Cycle Management of Industrial Land (LCMIL) in China," Land Use Policy, Elsevier, vol. 99(C).
    8. Sueyoshi, Toshiyuki & Wang, Derek, 2014. "Radial and non-radial approaches for environmental assessment by Data Envelopment Analysis: Corporate sustainability and effective investment for technology innovation," Energy Economics, Elsevier, vol. 45(C), pages 537-551.
    9. Wang, H., 2015. "A generalized MCDA–DEA (multi-criterion decision analysis–data envelopment analysis) approach to construct slacks-based composite indicator," Energy, Elsevier, vol. 80(C), pages 114-122.
    10. Tan, Xiujie & Xiao, Ziwei & Liu, Yishuang & Taghizadeh-Hesary, Farhad & Wang, Banban & Dong, Hanmin, 2022. "The effect of green credit policy on energy efficiency: Evidence from China," Technological Forecasting and Social Change, Elsevier, vol. 183(C).
    11. Xiaoling Xie & Lin Ye, 2023. "Reconstructing Rural Settlements Based on Structural Equation Modeling—Taking Hongshanyao Town of Jinchang City as an Example," Sustainability, MDPI, vol. 15(2), pages 1-13, January.
    12. Jin Xie & Yinying Cai & Hang Tang & Yuanqin Liao, 2020. "Housing Wealth Status and Informal Accumulation of Rural Villages at the Rural-Urban Fringe in Shanghai, China," Sustainability, MDPI, vol. 12(17), pages 1-23, August.
    13. Jianglin Lu & Keqiang Wang & Hongmei Liu, 2023. "Land Development Rights, Spatial Injustice, and the Economic Development in Net-Incremental Reduction Regions of Construction Land: Evidence from Shanghai, China," IJERPH, MDPI, vol. 20(3), pages 1-25, January.
    14. Sueyoshi, Toshiyuki & Goto, Mika, 2014. "Environmental assessment for corporate sustainability by resource utilization and technology innovation: DEA radial measurement on Japanese industrial sectors," Energy Economics, Elsevier, vol. 46(C), pages 295-307.
    15. Chen, Weidong & Geng, Wenxin, 2017. "Fossil energy saving and CO2 emissions reduction performance, and dynamic change in performance considering renewable energy input," Energy, Elsevier, vol. 120(C), pages 283-292.
    16. Zhaodi Lu & Mengyao Xu & Zhengfeng Zhang, 2022. "Analyzing Stakeholder Relationships for Construction Land Reduction Projects in Shanghai, China," Land, MDPI, vol. 11(12), pages 1-18, November.
    17. Chen, Chih Cheng, 2017. "Measuring departmental and overall regional performance: applying the multi-activity DEA model to Taiwan׳s cities/counties," Omega, Elsevier, vol. 67(C), pages 60-80.
    18. Yazhu Wang & Hui Zou & Xuejun Duan & Lingqing Wang, 2022. "Coordinated Evolution and Influencing Factors of Population and Economy in the Yangtze River Economic Belt," IJERPH, MDPI, vol. 19(21), pages 1-19, November.
    19. Liang-Han Ma & Jin-Chi Hsieh & Yung-Ho Chiu, 2020. "Comparing regional differences in global energy performance," Energy & Environment, , vol. 31(6), pages 943-960, September.
    20. Wang, Derek & Li, Shanling & Sueyoshi, Toshiyuki, 2014. "DEA environmental assessment on U.S. Industrial sectors: Investment for improvement in operational and environmental performance to attain corporate sustainability," Energy Economics, Elsevier, vol. 45(C), pages 254-267.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jlands:v:11:y:2022:i:7:p:1059-:d:861249. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.