IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v14y2022i9p5559-d809114.html
   My bibliography  Save this article

Identification of Relative Poverty Based on 2012–2020 NPP/VIIRS Night Light Data: In the Area Surrounding Beijing and Tianjin in China

Author

Listed:
  • Hao Liu

    (School of Management, China University of Mining and Technology, Beijing 100083, China
    These authors contributed equally to this work.)

  • Jingtao Wang

    (School of Management, China University of Mining and Technology, Beijing 100083, China
    These authors contributed equally to this work.)

  • Haibin Liu

    (School of Management, China University of Mining and Technology, Beijing 100083, China)

  • Yuzhuo Chen

    (School of Management, China University of Mining and Technology, Beijing 100083, China)

  • Xinghan Liu

    (School of Aeronautical Science and Engineering, Beihang University, Beijing 100191, China)

  • Yanlei Guo

    (School of Management, China University of Mining and Technology, Beijing 100083, China)

  • Hui Huang

    (School of Management, China University of Mining and Technology, Beijing 100083, China)

Abstract

As absolute poverty in China, measured by the current standard, is being eliminated, the focus of future poverty reduction projects will necessarily shift to addressing relative poverty. Contiguous poverty areas have been identified in Hebei province around Beijing and Tianjin (HABT), and this is not conducive to the coordinated development of the Beijing-Tianjin-Hebei region. The dynamic identification of relative poverty at the county level within the region must be the basis for formulating scientific strategies for poverty reduction. Night light (NTL) data can reveal socio-economic information and reflect human activities, and has a wide range of other applications for evaluating and identifying poverty. For this reason, NPP/VIIRS (Visible Infrared Imaging Radiometer Suite equipped on the Suomi National Polar orbiting Partnership satellite) NTL data from 2012 to 2020 were corrected, and NTL data for HABT were obtained. A multidimensional relative poverty index ( MRPI) that assesses being “free from worries over food and clothing and having access to compulsory education, basic medical services, and safe housing” using social statistical data was created with the analytic hierarchy process and entropy weight method. A panel regression model with fixed effects was established for MRPI and corrected NPP/VIIRS NTL data. The R 2 of fitting was 0.6578 and confirmed a strong correlation between MRPI and corrected NPP/VIIRS NTL data. Based on this, the MRPI estimation model was constructed based on the MRPI and corrected NPP/VIIRS NTL data, and passed the accuracy test. Finally, using the national list of poverty counties, it was verified that, at the county scale, the corrected NPP/VIIRS NTL data could effectively identify areas of relative poverty. This study lays the foundation for the use of NPP/VIIRS NTL data in the identification of areas of relative poverty. It provides a feasible method and data reference for analyzing relative poverty at a smaller scale. The dynamic identification of areas of relative poverty can also provide a basis for formulating scientific poverty reduction strategies.

Suggested Citation

  • Hao Liu & Jingtao Wang & Haibin Liu & Yuzhuo Chen & Xinghan Liu & Yanlei Guo & Hui Huang, 2022. "Identification of Relative Poverty Based on 2012–2020 NPP/VIIRS Night Light Data: In the Area Surrounding Beijing and Tianjin in China," Sustainability, MDPI, vol. 14(9), pages 1-23, May.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:9:p:5559-:d:809114
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/14/9/5559/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/14/9/5559/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Wan, Guanghua & Hu, Xiaoshan & Liu, Weiqun, 2021. "China's poverty reduction miracle and relative poverty: Focusing on the roles of growth and inequality," China Economic Review, Elsevier, vol. 68(C).
    2. Alkire, Sabina & Santos, Maria Emma, 2014. "Measuring Acute Poverty in the Developing World: Robustness and Scope of the Multidimensional Poverty Index," World Development, Elsevier, vol. 59(C), pages 251-274.
    3. Martin Ravallion & Shaohua Chen, 2011. "Weakly Relative Poverty," The Review of Economics and Statistics, MIT Press, vol. 93(4), pages 1251-1261, November.
    4. Maria Emma Santos & Pablo Villatoro, 2018. "A Multidimensional Poverty Index for Latin America," Review of Income and Wealth, International Association for Research in Income and Wealth, vol. 64(1), pages 52-82, March.
    5. Wenbin Pan & Hongming Fu & Peng Zheng, 2020. "Regional Poverty and Inequality in the Xiamen-Zhangzhou-Quanzhou City Cluster in China Based on NPP/VIIRS Night-Time Light Imagery," Sustainability, MDPI, vol. 12(6), pages 1-20, March.
    6. Misganaw Teshager Abeje & Atsushi Tsunekawa & Nigussie Haregeweyn & Zemen Ayalew & Zerihun Nigussie & Daregot Berihun & Enyew Adgo & Asres Elias, 2020. "Multidimensional Poverty and Inequality: Insights from the Upper Blue Nile Basin, Ethiopia," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 149(2), pages 585-611, June.
    7. David Griggs & Mark Stafford-Smith & Owen Gaffney & Johan Rockström & Marcus C. Öhman & Priya Shyamsundar & Will Steffen & Gisbert Glaser & Norichika Kanie & Ian Noble, 2013. "Sustainable development goals for people and planet," Nature, Nature, vol. 495(7441), pages 305-307, March.
    8. Tao Wang & Jian-sheng Chen & Ting Wang & Shuang Wang, 2015. "Entropy weight-set pair analysis based on tracer techniques for dam leakage investigation," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 76(2), pages 747-767, March.
    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. Deniz Sevinc, 2020. "How Poor is Poor? A novel look at multidimensional poverty in the UK," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 149(3), pages 833-859, June.
    2. Hong Sun & Xiaohong Li & Wenjing Li & Jun Feng, 2022. "Differences and Influencing Factors of Relative Poverty of Urban and Rural Residents in China Based on the Survey of 31 Provinces and Cities," IJERPH, MDPI, vol. 19(15), pages 1-15, July.
    3. Espinoza-Delgado, José & Silber, Jacques, 2018. "Multi-dimensional poverty among adults in Central America and gender differences in the three I’s of poverty: Applying inequality sensitive poverty measures with ordinal variables," MPRA Paper 88750, University Library of Munich, Germany.
    4. Pinaki Das & Bibek Paria & Shama Firdaush, 2021. "Juxtaposing Consumption Poverty and Multidimensional Poverty: A Study in Indian Context," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 153(2), pages 469-501, January.
    5. Wei Zou & Xiaopei Cheng & Zengzeng Fan & Wenxi Yin, 2023. "Multidimensional Relative Poverty in China: Identification and Decomposition," Sustainability, MDPI, vol. 15(6), pages 1-27, March.
    6. Verónica Amarante & Maira Colacce, 2022. "Multidimensional Poverty Among Older People in Five Latin American Countries," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 159(3), pages 945-965, February.
    7. Sam Jones, 2019. "Counting-based multidimensional poverty identification: From deprivation weights to bundles," WIDER Working Paper Series wp-2019-55, World Institute for Development Economic Research (UNU-WIDER).
    8. Espinoza-Delgado, José & Klasen, Stephan, 2018. "Gender and multidimensional poverty in Nicaragua: An individual based approach," World Development, Elsevier, vol. 110(C), pages 466-491.
    9. Sam Jones, 2022. "Extending multidimensional poverty identification: from additive weights to minimal bundles," The Journal of Economic Inequality, Springer;Society for the Study of Economic Inequality, vol. 20(2), pages 421-438, June.
    10. Qun Wang & Lu Shu & Xiaojun Lu, 2023. "Dynamics of multidimensional poverty and its determinants among the middle-aged and older adults in China," Palgrave Communications, Palgrave Macmillan, vol. 10(1), pages 1-9, December.
    11. Long Yang & Haiyang Lu & Sangui Wang & Meng Li, 2021. "Mobile Internet Use and Multidimensional Poverty: Evidence from A Household Survey in Rural China," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 158(3), pages 1065-1086, December.
    12. Shatakshee Dhongde & Robert Haveman, 2022. "Spatial and Temporal Trends in Multidimensional Poverty in the United States over the Last Decade," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 163(1), pages 447-472, August.
    13. Gallardo, Mauricio, 2022. "Measuring vulnerability to multidimensional poverty with Bayesian network classifiers," Economic Analysis and Policy, Elsevier, vol. 73(C), pages 492-512.
    14. Hongmei Chen & Weipeng Chao & Zhen Xue & Hanlin Wei & Qing Li, 2024. "The Assessment of Green Poverty Reduction Strategies in Ecologically Fragile Areas: A Case Study of Southern Xinjiang in China," Sustainability, MDPI, vol. 16(15), pages 1-15, July.
    15. Rodrigo García Arancibia & Ignacio Girela, 2024. "Graphical Representation of Multidimensional Poverty: Insights for Index Construction and Policy Making," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 172(2), pages 595-634, March.
    16. Leonardo Gasparini & Maria Emma Santos & Leopoldo Tornarolli, 2023. "Poverty in Latin America," Chapters, in: Jacques Silber (ed.), Research Handbook on Measuring Poverty and Deprivation, chapter 63, pages 673-684, Edward Elgar Publishing.
    17. Cunming Zou & Jianzhi Liu & Bencheng Liu & Xuhan Zheng & Yangang Fang, 2019. "Evaluating Poverty Alleviation by Relocation under the Link Policy: A Case Study from Tongyu County, Jilin Province, China," Sustainability, MDPI, vol. 11(18), pages 1-20, September.
    18. Ruikun Zheng & Peiyun Li, 2024. "A Study on the Measurement of Relative Poverty in Developing Countries with Large Populations," Sustainability, MDPI, vol. 16(13), pages 1-19, July.
    19. Ousmane Traoré, 2022. "The effect of income on health: evidence from the poverty gaps analysis method in the sub-Saharan Africa," International Review of Economics, Springer;Happiness Economics and Interpersonal Relations (HEIRS), vol. 69(3), pages 401-432, September.
    20. Stephan Klasen & Tatyana Krivobokova & Friederike Greb & Rahul Lahoti & Syamsul Hidayat Pasaribu & Manuel Wiesenfarth, 2016. "International income poverty measurement: which way now?," The Journal of Economic Inequality, Springer;Society for the Study of Economic Inequality, vol. 14(2), pages 199-225, June.

    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:jsusta:v:14:y:2022:i:9:p:5559-:d:809114. 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.