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Interannual Variation in Night-Time Light Radiance Predicts Changes in National Electricity Consumption Conditional on Income-Level and Region

Author

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  • Giacomo Falchetta

    (Fondazione Eni Enrico Mattei, Corso Magenta 63, 20123 Milano, Italy
    Catholic University of Milan, Largo Gemelli 1, 20123 Milan, Italy)

  • Michel Noussan

    (Fondazione Eni Enrico Mattei, Corso Magenta 63, 20123 Milano, Italy)

Abstract

Using remotely-sensed Suomi National Polar-orbiting Partnership (NPP)-VIIRS (Visible Infrared Imagery Radiometer Suite) night-time light (NTL) imagery between 2012 and 2016 and electricity consumption data from the IEA World Energy Balance database, we assemble a five-year panel dataset to evaluate if and to what extent NTL data are able to capture interannual changes in electricity consumption within different countries worldwide. We analyze the strength of the relationship both across World Bank income categories and between regional clusters, and we evaluate the heterogeneity of the link for different sectors of consumption. Our results show that interannual variation in nighttime light radiance is an effective proxy for predicting within-country changes in power consumption across all sectors, but only in lower-middle income countries. The result is robust to different econometric specifications. We discuss the key reasons behind this finding. The regions of Sub-Saharan Africa, Middle-East and North Africa, Latin America and the Caribbeans, and East Asia and the Pacific render a significant outcome, while changes in Europe, North America and South Asia are not successfully predicted by NTL. The designed methodological steps to process the raw data and the findings of the analysis improve the design and application of predictive models for electricity consumption based on NTL at different spatio-temporal scales.

Suggested Citation

  • Giacomo Falchetta & Michel Noussan, 2019. "Interannual Variation in Night-Time Light Radiance Predicts Changes in National Electricity Consumption Conditional on Income-Level and Region," Energies, MDPI, vol. 12(3), pages 1-20, January.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:3:p:456-:d:202341
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    References listed on IDEAS

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    1. Paul Anton Verwiebe & Stephan Seim & Simon Burges & Lennart Schulz & Joachim Müller-Kirchenbauer, 2021. "Modeling Energy Demand—A Systematic Literature Review," Energies, MDPI, vol. 14(23), pages 1-58, November.

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