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.

Night-time light (NTL) data have been widely used as a remote proxy for the economic performance of regions. The use of these data is more advantageous than the traditional census approach is due to its timeliness, low cost, and comparability between regions and countries. Several recent studies have explored monthly NTL composites produced by the Visible Infrared Imaging Radiometer Suite (VIIRS) and revealed a dimming of the light in some countries during the national lockdowns due to the COVID-19 pandemic. Here, we explicitly tested the extent to which the observed decrease in the amount of NTL is associated with the economic recession at the subnational level. Specifically, we explore how the association between Gross Domestic Product (GDP) and the amount of NTL is modulated by the pandemic and whether NTL data can still serve as a sufficiently reliable proxy for the economic performance of regions even during stressful pandemic periods. For this reason, we use the states of the US and quarterly periods within 2014–2021 as a case study. We start with building a linear mixed effects model linking the state-level quarterly GDPs with the corresponding pre-processed NTL data, additionally controlling only for a long-term trends and seasonal fluctuations. We intentionally do not include other socio-economic predictors, such as population density and structure, in the model, aiming to observe the ‘pure’ explanatory potential of NTL. As it is built only for the pre-COVID-19 period, this model demonstrates a rather good performance, with R2 = 0.60, while its extension across the whole period (2014–2021) leads to a considerable worsening of this (R2 = 0.42), suggesting that not accounting for the COVID-19 phenomenon substantially weakens the ‘natural’ GDP–NTL association. At the same time, the model’s enrichment with COVID-19 dummies restores the model fit to R2 = 0.62. As a plausible application, we estimated the state-level economic losses by comparing actual GDPs in the pandemic period with the corresponding predictions generated by the pre-COVID-19 model. The states’ vulnerability to the crisis varied from ~8 to ~18% (measured as a fraction of the pre-pandemic GDP level in the 4th quarter of 2019), with the largest losses being observed in states with a relatively low pre-pandemic GDP per capita, a low number of remote jobs, and a higher minority ratio.