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Spatiotemporal Pattern and Driving Factors of Carbon Emissions in Guangxi Based on Geographic Detectors

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  • Qianru Guo

    (Guangxi Key Laboratory of Environmental Processes and Remediation in Ecologically Fragile Regions, College of Environment and Resources, Guangxi Normal University, Guilin 541000, China)

  • Xiuting Lai

    (Guangxi Key Laboratory of Environmental Processes and Remediation in Ecologically Fragile Regions, College of Environment and Resources, Guangxi Normal University, Guilin 541000, China)

  • Yanhong Jia

    (Guangxi Key Laboratory of Environmental Processes and Remediation in Ecologically Fragile Regions, College of Environment and Resources, Guangxi Normal University, Guilin 541000, China)

  • Feili Wei

    (Guangxi Key Laboratory of Environmental Processes and Remediation in Ecologically Fragile Regions, College of Environment and Resources, Guangxi Normal University, Guilin 541000, China)

Abstract

Analysis of the spatiotemporal distribution pattern and driving factors of carbon emissions has been a focus of research in recent years. However, at the county level, analyses of the driving factors of carbon emissions are still relatively few. This study selected the Guangxi Zhuang Autonomous Region as the research subject, selecting the normalized difference vegetation index (NDVI), nighttime light index (NLI), gross domestic product (GDP), and population density (POP) as driving factors. Based on the geographic detector method, the spatiotemporal distribution pattern and driving factors of carbon emissions at the county level in Guangxi were investigated. The results show the following: (1) There are significant regional differences in the degree of change in carbon emissions. From 2005 to 2020, the total carbon emissions in Guangxi show an upward trend, presenting a “high in the south and low in the north” distribution characteristic, gradually forming a high-level region in the capital city of Nanning, the city of Liuzhou, and some coastal cities (such as the cities of Qinzhou, Beihai, and Fangchenggang) as the core of carbon emissions; (2) NDVI, NLI, GDP, and POP have a relatively high impact on the carbon emission pattern in Guangxi, and the impact of human activity intensity on carbon emissions is higher than that of the influencing factors of NDVI; (3) The interaction between NDVI, NLI, GDP, and POP has a significant impact on the carbon emission pattern. The aforementioned results can provide decision-making suggestions for the social and economic development of Guangxi, as well as the formulation of carbon sequestration policies.

Suggested Citation

  • Qianru Guo & Xiuting Lai & Yanhong Jia & Feili Wei, 2023. "Spatiotemporal Pattern and Driving Factors of Carbon Emissions in Guangxi Based on Geographic Detectors," Sustainability, MDPI, vol. 15(21), pages 1-14, October.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:21:p:15477-:d:1271508
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    1. Feipeng Guo & Linji Zhang & Zifan Wang & Shaobo Ji, 2022. "Research on Determining the Critical Influencing Factors of Carbon Emission Integrating GRA with an Improved STIRPAT Model: Taking the Yangtze River Delta as an Example," IJERPH, MDPI, vol. 19(14), pages 1-20, July.
    2. Zhang, Ning & Yu, Keren & Chen, Zhongfei, 2017. "How does urbanization affect carbon dioxide emissions? A cross-country panel data analysis," Energy Policy, Elsevier, vol. 107(C), pages 678-687.
    3. Fan, Jingjing & Wang, Jianliang & Qiu, Jixiang & Li, Nu, 2023. "Stage effects of energy consumption and carbon emissions in the process of urbanization: Evidence from 30 provinces in China," Energy, Elsevier, vol. 276(C).
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    1. Xiujuan Jiang & Jingyuan Sun & Jinchuan Huang & Nan Zhang & Linlin Xu & Zhenming Zhang, 2025. "Spatiotemporal Patterns and Influencing Factors of Carbon Emissions in the Yangtze River Basin: A Shrinkage Perspective," Sustainability, MDPI, vol. 17(5), pages 1-33, February.

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