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Addison, D., & Stewart, B. (2015). Nighttime Lights Revisited: The Use of Nighttime Lights Data as a Proxy for Economic Variables. World Bank Group.
Abstract: The growing availability of free or inexpensive satellite imagery has inspired many researchers to investigate the use of earth observation data for monitoring economic activity around the world. One of the most popular earth observation data sets is the so-called nighttime lights from the Defense Meteorological Satellite Program. Researchers have found positive correlations between nighttime lights and several economic variables. These correlations are based on data measured in levels, with a cross-section of observations within a single time period across countries or other geographic units. The findings suggest that nighttime lights could be used as a proxy for some economic variables, especially in areas or times where data are weak or unavailable. Yet, logic suggests that nighttime lights cannot serve as a good proxy for monitoring the within-in country growth rates all of these variables. Examples examined this paper include constant price gross domestic product, nonagricultural gross domestic product, manufacturing value
added, and capital stocks, as well as electricity consumption, total population, and urban population. The study finds that the Defense Meteorological Satellite Program data are quite noisy and therefore the resulting growth elasticities of Defense Meteorological Satellite Program nighttime lights with respect to most of these socioeconomic variables are low, unstable over time, and generate little explanatory power. The one exception for which Defense Meteorological Satellite Program nighttime lights could serve as a proxy is electricity consumption, measured in 10-year intervals. It is hoped that improved data from the recently launched Suomi National Polar-Orbiting Partnership satellite will help expand or improve these outcomes. Testing this should be an important next step.
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Chen, H., Zhang, X., Wu, R., & Cai, T. (2020). Revisiting the environmental Kuznets curve for city-level CO2 emissions: based on corrected NPP-VIIRS nighttime light data in China. Journal of Cleaner Production, , 121575.
Abstract: With the increasing trend of global warming, the Chinese government faces tremendous pressure to reduce CO2 emissions. The purpose of this study is to accurately measure CO2 emissions at the city scale in China and examine the environmental Kuznets curve, thereby providing a reference for decision-making. Corrected NPP-VIIRS nighttime light data were used to accurately estimate carbon dioxide emissions at the provincial and city scales in China. Then, based on the STRIPAT model, 291 cities in China were used to verify the environmental Kuznets curve. Our results show that on the provincial scale, the R2 between the estimated value and the statistical value of carbon dioxide reaches 0.85. Western cities in China emit more CO2, as do economically developed cities and industry- and mining-dominated cities. There are two CO2 emission hot spots in the north and one cold spot in the south. It was found that the environmental Kuznets curve on the city scale exists. This study has practical value in utilizing NPP-VIIRS data for the estimation of city CO2 emissions. The results also have academic value for determining factors that contribute to carbon dioxide emissions and can provide a reference for relevant decision makers. This study could be considered the first to simulate CO2 emissions at the provincial and city levels in China based on a NPP-VIIRS nighttime light model to explore the associated geographical distribution characteristics and potential influencing factors.
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Mann, M., Melaas, E., & Malik, A. (2016). Using VIIRS Day/Night Band to Measure Electricity Supply Reliability: Preliminary Results from Maharashtra, India. Remote Sensing, 8(9), 711.
Abstract: Unreliable electricity supplies are common in developing countries and impose large socio-economic costs, yet precise information on electricity reliability is typically unavailable. This paper presents preliminary results from a machine-learning approach for using satellite imagery of nighttime lights to develop estimates of electricity reliability for western India at a finer spatial scale. We use data from the Visible Infrared Imaging Radiometer Suite (VIIRS) onboard the Suomi National Polar Partnership (SNPP) satellite together with newly-available data from networked household voltage meters. Our results point to the possibilities of this approach as well as areas for refinement. With currently available training data, we find a limited ability to detect individual outages identified by household-level measurements of electricity voltage. This is likely due to the relatively small number of individual outages observed in our preliminary data. However, we find that the approach can estimate electricity reliability rates for individual locations fairly well, with the predicted versus actual regression yielding an R2 > 0.5. We also find that, despite the after midnight overpass time of the SNPP satellite, the reliability estimates derived are representative of daytime reliability.
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