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Estimating global economic well-being with unlit settlements

Author

Listed:
  • Ian McCallum

    (International Institute for Applied Systems Analysis)

  • Christopher Conrad Maximillian Kyba

    (GFZ German Research Centre for Geosciences, Telegrafenberg)

  • Juan Carlos Laso Bayas

    (International Institute for Applied Systems Analysis)

  • Elena Moltchanova

    (University of Canterbury)

  • Matt Cooper

    (Harvard University)

  • Jesus Crespo Cuaresma

    (International Institute for Applied Systems Analysis
    Vienna University of Economics and Business)

  • Shonali Pachauri

    (International Institute for Applied Systems Analysis)

  • Linda See

    (International Institute for Applied Systems Analysis)

  • Olga Danylo

    (International Institute for Applied Systems Analysis)

  • Inian Moorthy

    (International Institute for Applied Systems Analysis)

  • Myroslava Lesiv

    (International Institute for Applied Systems Analysis)

  • Kimberly Baugh

    (University of Colorado, 216 UCB)

  • Christopher D. Elvidge

    (Earth Observation Group, Payne Institute for Public Policy, Colorado School of Mines)

  • Martin Hofer

    (International Institute for Applied Systems Analysis)

  • Steffen Fritz

    (International Institute for Applied Systems Analysis)

Abstract

It is well established that nighttime radiance, measured from satellites, correlates with economic prosperity across the globe. In developing countries, areas with low levels of detected radiance generally indicate limited development – with unlit areas typically being disregarded. Here we combine satellite nighttime lights and the world settlement footprint for the year 2015 to show that 19% of the total settlement footprint of the planet had no detectable artificial radiance associated with it. The majority of unlit settlement footprints are found in Africa (39%), rising to 65% if we consider only rural settlement areas, along with numerous countries in the Middle East and Asia. Significant areas of unlit settlements are also located in some developed countries. For 49 countries spread across Africa, Asia and the Americas we are able to predict and map the wealth class obtained from ~2,400,000 geo-located households based upon the percent of unlit settlements, with an overall accuracy of 87%.

Suggested Citation

  • Ian McCallum & Christopher Conrad Maximillian Kyba & Juan Carlos Laso Bayas & Elena Moltchanova & Matt Cooper & Jesus Crespo Cuaresma & Shonali Pachauri & Linda See & Olga Danylo & Inian Moorthy & Myr, 2022. "Estimating global economic well-being with unlit settlements," Nature Communications, Nature, vol. 13(1), pages 1-8, December.
    • Handle: RePEc:nat:natcom:v:13:y:2022:i:1:d:10.1038_s41467-022-30099-9
    DOI: 10.1038/s41467-022-30099-9
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    Cited by:

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    2. Muchen Zheng & Wenli Huang & Gang Xu & Xi Li & Limin Jiao, 2023. "Spatial gradients of urban land density and nighttime light intensity in 30 global megacities," Palgrave Communications, Palgrave Macmillan, vol. 10(1), pages 1-11, December.
    3. Duan, Wenqi & Jiang, Mingming & Qi, Jianhong, 2024. "Agricultural certification, market access and rural economic growth: Evidence from poverty-stricken counties in China," Economic Analysis and Policy, Elsevier, vol. 81(C), pages 99-114.

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