Note: ReGNL: Rapid Prediction of GDP during Disruptive Events using Nightlights

Musthyala, Rushabh; Rudrajit, Kargupta,; Hritish, Jain,; Chakraborty, Dipanjan

doi:10.1145/3530190.3534849

Cited by 1 publication

(1 citation statement)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Ghosh et al (2020) showed nighttime light data are a good measure of the economic cost of the pandemic in the first months after COVID-19 began with a highly accurate projection of changes in GDP in India. Musthyala et al (2022) showed that in the U.S., the Regional GDP Night Light (ReGNL) model is disruption-agnostic and can be used to predict the GDP for both the normal years (2019) and for the years with the pandemic (2020). Elvidge et al (2020) used VIIRS Day/Night Band (DNB) nightly and monthly composites to examine the economic impacts of the pandemic in China and suggested this can be used in other country cases to examine the post-pandemic economic declines and recoveries.…”

Section: Introductionmentioning

confidence: 99%

Using Satellite Nighttime Light to Measure Socio-Economic Activity in Africa Before and After COVID

Lin

Rybnikova

2023

J Stud Res

View full text Add to dashboard Cite

Using satellite nighttime light (NTL) data as a proxy to measure socio-economic activity in normal times has been well-established in the remote sensing literature. In the recent years, the NTL composites produced by the Visible Infrared Imaging Radiometer Suite (VIIRS) revealed a dimming of light in major cities during the COVID pandemic in large countries like China, India, and U.S. To test whether NTL remained a valid proxy of economic and human activity during upheaval times at the country level, this paper examined the association between NTL and GDP, CO2 emissions, and electricity consumption in all countries in Africa in the period of 2014-2021. The results indicated that NTL is associated with these three socio-economic indicators in a significant manner before and after the COVID pandemic. The model demonstrated high performance of NTL as a proxy for GDP and CO2 emissions in both periods while less so for electricity consumption (with R2=0.53, 0.48, 0.36, respectively, during pre-COVID period of 2014-19; and with R2=0.49, 0.45, 0.26, respectively, for 2014-2021 with COVID dummies). As NTL data are free and available at granular spatial and temporal levels for most areas on Earth with just a short time lag, the methodology offered an alternative to consistently measure economic and human activities and impact on the environment during normal times and after external shocks. This has the potential to fill data gaps, including for countries with weak capacity, and aid policy making to support sustainable green growth and provide information swiftly for post-disaster recovery.

show abstract

Section: Introductionmentioning

confidence: 99%