IDEAS home Printed from https://ideas.repec.org/a/gam/jlands/v13y2024i9p1524-d1481609.html
   My bibliography  Save this article

Assessing Uneven Regional Development Using Nighttime Light Satellite Data and Machine Learning Methods: Evidence from County-Level Improved HDI in China

Author

Listed:
  • Xiping Zhang

    (College of Resources and Environment, Shanxi University of Finance and Economics, No.140, Wucheng Road, Taiyuan 030006, China)

  • Jianbin Xu

    (College of Resources and Environment, Shanxi University of Finance and Economics, No.140, Wucheng Road, Taiyuan 030006, China)

  • Saiying Zhong

    (College of Resources and Environment, Shanxi University of Finance and Economics, No.140, Wucheng Road, Taiyuan 030006, China)

  • Ziheng Wang

    (College of Resources and Environment, Shanxi University of Finance and Economics, No.140, Wucheng Road, Taiyuan 030006, China)

Abstract

Uneven regional development has long been a focal issue for both academia and policymakers, with numerous studies over the past decades actively engaging in discussions on measuring regional development disparities. Generally, most existing studies measure the Human Development Index (HDI) using relatively simple indicators, with a focus on national and provincial scales. As a crucial component of regional development, counties can directly reflect the regional characteristics of socio-economic progress. This study employs a multi-dimensional approach to develop an improved Human Development Index (improved HDI) system, using machine learning techniques to establish the relationship between nighttime light (NTL) data and the improved HDI. Subsequently, NTL data are utilized to infer the spatial distribution characteristics of the improved HDI across China’s county-level regions. The improved HDI for county-level areas in the Ningxia Hui Autonomous Region was validated using a machine learning model, resulting in a Pearson correlation coefficient of 0.93. The adjusted R-squared value for the linear fit was 0.86, and the residuals were relatively balanced, ensuring the accuracy of the simulations. This study reveals that 1439 county-level units, representing 50% of all county-level units in China, have development levels at or above the medium level. At the provincial and national levels, the improved HDI shows significant clustering, characterized by a multi-center pattern with declining diffusion. The spatial distribution of the improved Human Development Index remains closely associated with the natural geographic background and socio-economic development levels of the county regions. Lower HDI values are predominantly found in the inland areas of central and western China, often in ecologically sensitive areas, inter-provincial border zones, and mountainous regions of mainland China, sometimes forming contiguous distribution patterns. This underscores the need for the government and society to focus more on these specific geographic development areas, promoting continuous improvements in health, education, and living standards to achieve coordinated regional development.

Suggested Citation

  • Xiping Zhang & Jianbin Xu & Saiying Zhong & Ziheng Wang, 2024. "Assessing Uneven Regional Development Using Nighttime Light Satellite Data and Machine Learning Methods: Evidence from County-Level Improved HDI in China," Land, MDPI, vol. 13(9), pages 1-19, September.
    • Handle: RePEc:gam:jlands:v:13:y:2024:i:9:p:1524-:d:1481609
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2073-445X/13/9/1524/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2073-445X/13/9/1524/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Fleisher, Belton & Li, Haizheng & Zhao, Min Qiang, 2010. "Human capital, economic growth, and regional inequality in China," Journal of Development Economics, Elsevier, vol. 92(2), pages 215-231, July.
    2. Doll, Christopher N.H. & Muller, Jan-Peter & Morley, Jeremy G., 2006. "Mapping regional economic activity from night-time light satellite imagery," Ecological Economics, Elsevier, vol. 57(1), pages 75-92, April.
    3. Michael Storper, 2018. "Separate Worlds? Explaining the current wave of regional economic polarization," Journal of Economic Geography, Oxford University Press, vol. 18(2), pages 247-270.
    4. James Foster & Luis Lopez-Calva & Miguel Szekely, 2005. "Measuring the Distribution of Human Development: methodology and an application to Mexico," Journal of Human Development and Capabilities, Taylor & Francis Journals, vol. 6(1), pages 5-25.
    5. Brock, Gregory, 2019. "A remote sensing look at the economy of a Russian region (Rostov) adjacent to the Ukrainian crisis," Journal of Policy Modeling, Elsevier, vol. 41(2), pages 416-431.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Qingchun Guan & Tianya Meng & Chengyang Guan & Junwen Chen & Hui Li & Xu Zhou, 2024. "Multi-Scale Analysis of Carbon Emissions in Coastal Cities Based on Multi-Source Data: A Case Study of Qingdao, China," Land, MDPI, vol. 13(11), pages 1-20, November.

    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. Andrés Rodríguez-Pose & Michael Storper, 2020. "Housing, urban growth and inequalities: The limits to deregulation and upzoning in reducing economic and spatial inequality," Urban Studies, Urban Studies Journal Limited, vol. 57(2), pages 223-248, February.
    2. Fan, Fei & Dai, Shangze & Yang, Bo & Ke, Haiqian, 2023. "Urban density, directed technological change, and carbon intensity: An empirical study based on Chinese cities," Technology in Society, Elsevier, vol. 72(C).
    3. Jeni Klugman & Francisco Rodríguez & Hyung-Jin Choi, 2011. "The HDI 2010: new controversies, old critiques," The Journal of Economic Inequality, Springer;Society for the Study of Economic Inequality, vol. 9(2), pages 249-288, June.
    4. Marco Manacorda & Guido Tabellini & Andrea Tesei, 2022. "Mobile internet and the rise of political tribalism in Europe," CEP Discussion Papers dp1877, Centre for Economic Performance, LSE.
    5. Qing Li & Long Hai Vo, 2021. "Intangible Capital and Innovation: An Empirical Analysis of Vietnamese Enterprises," Economics Discussion / Working Papers 21-02, The University of Western Australia, Department of Economics.
    6. Corral, Leonardo R. & Schling, Maja, 2017. "The impact of shoreline stabilization on economic growth in small island developing states," Journal of Environmental Economics and Management, Elsevier, vol. 86(C), pages 210-228.
    7. Yao Zhao & Xuena Kong & Mahmood Ahmad & Zahoor Ahmed, 2023. "Digital Economy, Industrial Structure, and Environmental Quality: Assessing the Roles of Educational Investment, Green Innovation, and Economic Globalization," Sustainability, MDPI, vol. 15(3), pages 1-24, January.
    8. Boslett, Andrew & Hill, Elaine & Ma, Lala & Zhang, Lujia, 2021. "Rural light pollution from shale gas development and associated sleep and subjective well-being," Resource and Energy Economics, Elsevier, vol. 64(C).
    9. Jing Li & Tsun Se Cheong & Jianfa Shen & Dahai Fu, 2019. "Urbanization And Rural–Urban Consumption Disparity: Evidence From China," The Singapore Economic Review (SER), World Scientific Publishing Co. Pte. Ltd., vol. 64(04), pages 983-996, September.
    10. Zhou, Yixiao & Tyers, Rod, 2019. "Automation and inequality in China," China Economic Review, Elsevier, vol. 58(C).
    11. Anping Chen & Nicolaas Groenewold, 2017. "An increase in the retirement age in China: the regional economic effects," Applied Economics, Taylor & Francis Journals, vol. 49(7), pages 702-721, February.
    12. Brada, Josef C. & Gajewski, Paweł & Kutan, Ali M., 2021. "Economic resiliency and recovery, lessons from the financial crisis for the COVID-19 pandemic: A regional perspective from Central and Eastern Europe," International Review of Financial Analysis, Elsevier, vol. 74(C).
    13. Andrea Pontiggia & Lala Hu & Marco Savorgnan, 2013. "ChinaÕs Human Resources Development: Recent Evolution and Implications for the Global Market," Working Papers 29, Venice School of Management - Department of Management, Università Ca' Foscari Venezia.
    14. Felix Roth & Anna-Elisabeth Thum, 2022. "Intangible Capital and Labor Productivity Growth: Panel Evidence for the EU from 1998–2005," Contributions to Economics, in: Intangible Capital and Growth, chapter 0, pages 101-128, Springer.
    15. Yuwan Duan & Erik Dietzenbacher & Bart Los & Ruochen Dai, 2023. "Regional inequality in China during its rise as a giant exporter: A value chain analysis," Journal of Regional Science, Wiley Blackwell, vol. 63(1), pages 148-172, January.
    16. Zhang, Xiaobei & Wang, Xiaojun, 2021. "Measures of human capital and the mechanics of economic growth," China Economic Review, Elsevier, vol. 68(C).
    17. Yong He & Hong Zhou, 2011. "Estimating Chinese Interprovincial OutPut Spillovers with Provincial Input-Output Tables," CERDI Working papers halshs-00553102, HAL.
    18. Natalya Rybnikova & Boris Portnov, 2015. "Using light-at-night (LAN) satellite data for identifying clusters of economic activities in Europe," Letters in Spatial and Resource Sciences, Springer, vol. 8(3), pages 307-334, November.
    19. Leßmann, Ortrud, 2011. "Empirische Studien zum Capability Ansatz auf der Grundlage von Befragungen: Ein Überblick," UFZ Discussion Papers 4/2011, Helmholtz Centre for Environmental Research (UFZ), Division of Social Sciences (ÖKUS).
    20. Daniela Smiraglia & Luca Salvati & Gianluca Egidi & Rosanna Salvia & Antonio Giménez-Morera & Rares Halbac-Cotoara-Zamfir, 2021. "Toward a New Urban Cycle? A Closer Look to Sprawl, Demographic Transitions and the Environment in Europe," Land, MDPI, vol. 10(2), pages 1-14, January.

    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:13:y:2024:i:9:p:1524-:d:1481609. 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.