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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
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    References listed on IDEAS

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