Exploring the Relation between NPP-VIIRS Nighttime Lights and Carbon Footprint, Population Growth, and Energy Consumption in the UAE

TL;DR

This study investigates the correlation between NPP-VIIRS nighttime lights and socioeconomic indicators like carbon footprint, population, and energy use in the UAE, showing strong potential for remote sensing in urbanization and emission estimation.

Contribution

It demonstrates the viability of using NPP-VIIRS nighttime lights as proxies for socioeconomic and environmental indicators in oil-producing countries.

Findings

High correlation (>0.9) between NTLs and population.

Good correlation (0.7) between NTLs and carbon emissions.

Correlation varies across cities, e.g., Dubai vs. Al-Ain.

Abstract

Due to global warming and its detrimental effect, every country is responsible to join the global effort to reduce carbon emissions. In order to improve the mitigation plan of climate change, accurate es-timates of carbon emissions, population, and electricity consumption are critical. Carbon footprint is significantly linked to the socioeconomic development of the country which can be reflected in the city's infrastructure and urbanization. We may be able to estimate the carbon footprint, population growth, and electricity consumption of a city by observing the nighttime light reflecting its urbanization. This is more challenging in oil-producing countries where urbanization can be more complicated. In this study, we are therefore investigating the possibility of correlating the remotely sensed NPP-VIIRS Nighttime light (NTL) estimation with the aforementioned socioeconomic indicators. Daily NPP-VIIRS NTL were obtained for the period between 2012 to 2021 for the United Arab Emirates (UAE) which is one of the top oil producing countries. The socioeconomic indicators of the UAE, including the population, electricity consumption, and carbon dioxide emissions, have been obtained for the same period. The analysis of the correlation between the NTLs and the population indicates that there is a high correlation of more than 0.9. There is also a very good correlation of 0.7 between NTLs and carbon emissions and electricity consumption. However, these correlations differ from one city to another. For example, Dubai has shown the highest correlation between population and NTLs (R2 > 0.8). However, the correlation was the lowest in Al-Ain, a rural city (R2 < 0.4) with maximum electricity consumption of 1.1E04 GWh. These results demonstrate that NTLs can be considered as a promising proxy for carbon footprint and urbanization in oil-producing regions.