Spatial associations between electric power consumption in three major urban agglomerations of China via a len of nighttime light index

Abstract

Electricity constitutes a fundamental pillar of both the national economy and contemporary lifestyles. Monitoring electric power consumption (EPC) has important implications for energy planning, energy conservation and emission reduction, energy security, and smart city development. However, the current monitoring and evaluation of EPC is less accurate and does not allow for real-time monitoring and evaluation of EPC. This study established an EPC assessment model based on EPC data, nighttime light remote sensing technology, and GIScience methodology, aiming to analyze the spatiotemporal variation of EPC in three major urban agglomerations of China from 2012 to 2020 and estimate EPC in 2025. Furthermore, the spatial correlation of EPC was explored using Moran’ s I spatial analysis method. The results indicate that the established model has an average accuracy of 77.56% and can be used for accurate and real-time estimation of EPC. The EPC showed an increasing trend from 2012 to 2020, with the Yangtze River Delta urban agglomeration (YRD) exhibiting the highest growth rate, as high as 49.60%. The EPC in the Beijing-Tianjin-Hebei urban agglomeration (BTH) showed a negative spatial correlation. However, the YRD and the Guangdong-Hong Kong-Macao Greater Bay Area urban agglomeration (GBA) exhibited significant positive spatial correlation in EPC. The findings of this study serve a scientific basis and reference data for the development of energy policies and strategies. Furthermore, this study can help to achieve the “carbon peaking and carbon neutrality goals” proposed by the Chinese government.

Graphical abstract

Keywords

nighttime lights / EPC / dynamic monitoring / spatial analysis / three major urban agglomerations of China

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Acknowledgement

This work was supported by the National Natural Science Foundation of China (Grant No. 42201077); the Natural Science Foundation of Shandong Province (No. ZR2021QD074); the China Postdoctoral Science Foundation (No. 2023M732105); the Youth Innovation Team Project of Higher School in Shandong Province, China (No. 2024KJH087).

Data Availability Statement

The data sets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

Competing interests

The authors declare that they have no competing interests.

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