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Spatio-Temporal Dynamics and Driving Forces of Multi-Scale Emissions Based on Nighttime Light Data: A Case Study of the Pearl River Delta Urban Agglomeration

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  • Yajing Liu

    (College of Mining Engineering, North China University of Science and Technology, Tangshan 063210, China
    Tangshan Key Laboratory of Resources and Environmental Remote Sensing, Tangshan 063210, China
    Hebei Industrial Technology Institute of Mine Ecological Remediation, Tangshan 063210, China
    Hebei Key Laboratory of Mining Development and Security Technology, Tangshan 063210, China)

  • Shuai Zhou

    (College of Mining Engineering, North China University of Science and Technology, Tangshan 063210, China)

  • Ge Zhang

    (No.2 Geological Brigade of Hebei Bureau of Geology and Mineral Resources Exploration, Tangshan 063210, China)

Abstract

It is of great significance to formulate differentiated carbon emission reduction policies to clarify spatio-temporal characteristics and driving factors of carbon emissions in different cities and cities at different scales. By fitting nighttime light data (NTL) of long time series from 2000 to 2020, a carbon emission estimation model of Pearl River Delta urban agglomeration at city, county, and grid unit levels was built to quickly and accurately estimate carbon emission in the Delta cities above county level. Combining spatial statistics, spatial autocorrelation, Emerging Spatio-Temporal Hotspot Analysis (ES-THA), and Theil index (TL), this study explored the spatio-temporal differentiation of urban carbon emissions in the Delta and used a geographical detector to determine the influencing factors of the differentiation. The results of the study showed that NTL could replace a statistical yearbook in calculating carbon emissions of cities at or above county level. The calculation error was less than 18.7385% in the Delta. The three levels of carbon emissions in the Delta increased in a fluctuating manner, and the spatial distribution difference in carbon emissions at the municipal and county levels was small. Therefore, a combination of municipal and county scales can be implemented to achieve precise emission reduction at both macro and micro levels. The central and eastern parts of the agglomeration, including Guangzhou (Gz), Shenzhen (Sz), Zhongshan (Zs), and Huizhou (Hz), were a high-value clustering and spatio-temporal hot spots of carbon emissions. Zhaoqing (Zq) in the northwestern part of the agglomeration has always been a low-value clustering and a spatio-temporal cold spot because of its population, economy, and geographical location. The carbon emission differences in the Delta cities were mainly caused by carbon emission differences within the cities at the municipal level, and the cities faced the challenge of regional differences in the reduction in per capita carbon emissions. As the most influential single factor, spatial interaction between economic development and various factors was the main driving force for the growth of carbon emissions. Therefore, the results of this study provide a scientific theory and information support for carbon emission estimation and prediction, differentiated emission reduction measures, and carbon neutrality of cities in the Delta.

Suggested Citation

  • Yajing Liu & Shuai Zhou & Ge Zhang, 2023. "Spatio-Temporal Dynamics and Driving Forces of Multi-Scale Emissions Based on Nighttime Light Data: A Case Study of the Pearl River Delta Urban Agglomeration," Sustainability, MDPI, vol. 15(10), pages 1-24, May.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:10:p:8234-:d:1150244
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