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Cross Reference of GDP Decrease with Nighttime Light Data via Remote Sensing Diagnosis

Author

Listed:
  • Robert Duerler

    (State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China
    University of Chinese Academy of Sciences, Beijing 100094, China)

  • Chunxiang Cao

    (State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China)

  • Bo Xie

    (China Aerospace Science and Technology Corporation, The 9th Academy Unmanned System Center, Beijing 100094, China
    Aerospace Times Feihong Technology Company Limited, Beijing 100094, China)

  • Zhibin Huang

    (State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China
    University of Chinese Academy of Sciences, Beijing 100094, China)

  • Yiyu Chen

    (China Siwei Surveying and Mapping Technology Co., Ltd., China Aerospace Science and Technology Corporation, Beijing 100094, China)

  • Kaimin Wang

    (State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China
    University of Chinese Academy of Sciences, Beijing 100094, China)

  • Min Xu

    (State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China)

  • Yilin Lu

    (China Center for Resources Satellite Data and Applications, Beijing 100094, China)

Abstract

Nighttime light data is a mainstay method in confirming and supporting other traditional economic data indicators, which in turn influence business and policy-making decisions. Accuracy in and clear definition of economic data and its related indicators are thus of great importance not only for analysis of urban development and related policies seeking sustainable development, but also plays a key role in whether or not these policies are successful. Discovering and recognizing the applications and limitations of nighttime light and other peripheral data could prove helpful in future data analysis and sustainable development policy. One possible limitation could exist in GDP decrease, and whether or not nighttime light would decrease accordingly, as most studies show nighttime light increase confirms economic growth, thus affecting the reliability of the data’s correlation with economic data. This study utilizes nighttime light data in a cross-reference with GDP data during instances of global GDP shrinkage over the years of 2007–2017, split between 2007–2012 for the DMSP dataset and 2013–2017 for the VIIRs dataset. It seeks to establish through linear regression whether or not yearly average nighttime light data products show a positive correlation even during periods of economic decline, thereby providing a clearer understanding of the strengths and limitations of NTL as an economic validation indicator. Analysis shows that both years of global GDP decrease in turn also displayed global nighttime light decrease, in addition to linear regression giving satisfactory results pointing to a positive correlation over the timespan. The VIIRS data series resulted in higher regression coefficients, which is in line with the results of previous literature.

Suggested Citation

  • Robert Duerler & Chunxiang Cao & Bo Xie & Zhibin Huang & Yiyu Chen & Kaimin Wang & Min Xu & Yilin Lu, 2023. "Cross Reference of GDP Decrease with Nighttime Light Data via Remote Sensing Diagnosis," Sustainability, MDPI, vol. 15(8), pages 1-13, April.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:8:p:6900-:d:1127646
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    References listed on IDEAS

    as
    1. Zhaoxin Dai & Yunfeng Hu & Guanhua Zhao, 2017. "The Suitability of Different Nighttime Light Data for GDP Estimation at Different Spatial Scales and Regional Levels," Sustainability, MDPI, vol. 9(2), pages 1-15, February.
    2. Jinpei Ou & Xiaoping Liu & Xia Li & Meifang Li & Wenkai Li, 2015. "Evaluation of NPP-VIIRS Nighttime Light Data for Mapping Global Fossil Fuel Combustion CO2 Emissions: A Comparison with DMSP-OLS Nighttime Light Data," PLOS ONE, Public Library of Science, vol. 10(9), pages 1-20, September.
    3. Watanabe, Chihiro & Tou, Yuji & Neittaanmäki, Pekka, 2018. "A new paradox of the digital economy - Structural sources of the limitation of GDP statistics," Technology in Society, Elsevier, vol. 55(C), pages 9-23.
    4. Thiemo Fetzer & Oliver Pardo & Amar Shanghavi, 2018. "More than an urban legend: the short- and long-run effects of unplanned fertility shocks," Journal of Population Economics, Springer;European Society for Population Economics, vol. 31(4), pages 1125-1176, October.
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