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An assessment of global electric power consumption using the Defense Meteorological Satellite Program-Operational Linescan System nighttime light imagery

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  • Lu, Linlin
  • Weng, Qihao
  • Xie, Yanhua
  • Guo, Huadong
  • Li, Qingting

Abstract

Industrialization and urbanization have led to a remarkable increase of electric power consumption (EPC) during the past decades. To assess the changing patterns of EPC at the global scale, this study utilized nighttime lights in conjunction with population and built-up datasets to map EPC at 1 km resolution. Firstly, the inter-calibrated nighttime light data were enhanced using the V4.0 Gridded Population Density data and the Global Human Settlement Layer. Secondly, linear models were calibrated to relate EPC to the enhanced nighttime light data; these models were then employed to estimate per-pixel EPC in 2000 and 2013. Finally, the spatiotemporal patterns of EPC between the periods were analyzed at the country, continental, and global scales. The evaluation of the EPC estimation shows a reasonable accuracy at the provincial scale with R2 of 0.8429. Over 30% of the human settlements in Asia, Europe, and North America showed apparent EPC growth. At the national scale, moderate and high EPC growth was observed in 45% of the built-up areas in East Asia. The spatial clustering patterns revealed that EPC decreased in Russia and the Western Europe. This study provides fresh insight into the spatial pattern and variations of global electric power consumption.

Suggested Citation

  • Lu, Linlin & Weng, Qihao & Xie, Yanhua & Guo, Huadong & Li, Qingting, 2019. "An assessment of global electric power consumption using the Defense Meteorological Satellite Program-Operational Linescan System nighttime light imagery," Energy, Elsevier, vol. 189(C).
    • Handle: RePEc:eee:energy:v:189:y:2019:i:c:s0360544219320468
    DOI: 10.1016/j.energy.2019.116351
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    Cited by:

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    4. Hu, Ting & Wang, Ting & Yan, Qingyun & Chen, Tiexi & Jin, Shuanggen & Hu, Jun, 2022. "Modeling the spatiotemporal dynamics of global electric power consumption (1992–2019) by utilizing consistent nighttime light data from DMSP-OLS and NPP-VIIRS," Applied Energy, Elsevier, vol. 322(C).
    5. Bin Guo & Yi Bian & Lin Pei & Xiaowei Zhu & Dingming Zhang & Wencai Zhang & Xianan Guo & Qiuji Chen, 2022. "Identifying Population Hollowing Out Regions and Their Dynamic Characteristics across Central China," Sustainability, MDPI, vol. 14(16), pages 1-19, August.
    6. Pengpeng Chang & Xueru Pang & Xiong He & Yiting Zhu & Chunshan Zhou, 2022. "Exploring the Spatial Relationship between Nighttime Light and Tourism Economy: Evidence from 31 Provinces in China," Sustainability, MDPI, vol. 14(12), pages 1-22, June.
    7. Wang, Jiaxin & Lu, Feng, 2021. "Modeling the electricity consumption by combining land use types and landscape patterns with nighttime light imagery," Energy, Elsevier, vol. 234(C).

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