探索NPP-VIIRS夜间灯光与阿联酋碳足迹、人口增长和能源消耗之间的关系
Exploring the Relation between NPP-VIIRS Nighttime Lights and Carbon Footprint, Population Growth, and Energy Consumption in the UAE
AI总结：
本研究利用2012至2021年阿联酋NPP-VIIRS夜间灯光数据，分析其与人口、电力消耗和碳排放的相关性，发现夜间灯光可作为产油地区碳足迹与城市化的有效代理指标。
摘要：
由于全球变暖及其有害影响，每个国家都有责任加入全球减少碳排放的努力。为了改进气候变化的减缓计划，准确估算碳排放、人口和电力消耗至关重要。碳足迹与国家的社会经济发展显著相关，这种发展可以反映在城市的基础设施和城市化中。通过观测反映城市化的夜间灯光，我们或许能够估算一个城市的碳足迹、人口增长和电力消耗。这在城市化可能更为复杂的产油国中更具挑战性。因此，在本研究中，我们探讨了将遥感NPP-VIIRS夜间灯光（NTL）估算与上述社会经济指标相关联的可能性。我们获取了阿拉伯联合酋长国（UAE）——全球主要产油国之一——2012年至2021年期间的每日NPP-VIIRS夜间灯光数据。同一时期还获取了阿联酋的社会经济指标，包括人口、电力消耗和二氧化碳排放量。对夜间灯光与人口之间相关性的分析表明，二者存在超过0.9的高相关性。夜间灯光与碳排放和电力消耗之间也存在非常好的0.7相关性。然而，这些相关性因城市而异。例如，迪拜在人口与夜间灯光之间表现出最高的相关性（R² > 0.8）。然而，在艾因这个农村城市，相关性最低（R² < 0.4），其最大电力消耗为1.1E04 GWh。这些结果表明，夜间灯光可以被视为产油地区碳足迹和城市化的一个有前景的代理指标。
英文摘要：
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.
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