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The Nonlinear Impact of Mobile Human Activities on Vegetation Change in the Guangdong–Hong Kong–Macao Greater Bay Area

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  • Qionghuan Liu

    (Research Institute for Smart Cities, School of Architecture and Urban Planning, Shenzhen University, Shenzhen 518060, China
    MNR Technology Innovation Center of Territorial & Spatial Big Data, MNR Key Laboratory for Geo-Environmental Monitoring of Great Bay Area, Guangdong Key Laboratory of Urban Informatics, Shenzhen 518060, China)

  • Renzhong Guo

    (Research Institute for Smart Cities, School of Architecture and Urban Planning, Shenzhen University, Shenzhen 518060, China
    MNR Technology Innovation Center of Territorial & Spatial Big Data, MNR Key Laboratory for Geo-Environmental Monitoring of Great Bay Area, Guangdong Key Laboratory of Urban Informatics, Shenzhen 518060, China)

  • Zhengdong Huang

    (Research Institute for Smart Cities, School of Architecture and Urban Planning, Shenzhen University, Shenzhen 518060, China
    MNR Technology Innovation Center of Territorial & Spatial Big Data, MNR Key Laboratory for Geo-Environmental Monitoring of Great Bay Area, Guangdong Key Laboratory of Urban Informatics, Shenzhen 518060, China)

  • Biao He

    (Research Institute for Smart Cities, School of Architecture and Urban Planning, Shenzhen University, Shenzhen 518060, China
    MNR Technology Innovation Center of Territorial & Spatial Big Data, MNR Key Laboratory for Geo-Environmental Monitoring of Great Bay Area, Guangdong Key Laboratory of Urban Informatics, Shenzhen 518060, China)

  • Xiaoming Li

    (Research Institute for Smart Cities, School of Architecture and Urban Planning, Shenzhen University, Shenzhen 518060, China
    MNR Technology Innovation Center of Territorial & Spatial Big Data, MNR Key Laboratory for Geo-Environmental Monitoring of Great Bay Area, Guangdong Key Laboratory of Urban Informatics, Shenzhen 518060, China)

Abstract

Vegetation is essential for ecosystem function and sustainable urban development. In the context of urbanization, the Guangdong–Hong Kong–Macao Greater Bay Area (GBA), as the typical urban-dominated region, has experienced a remarkable increase in social and economic activities. Their impact on vegetation is of great significance but unclear, as interannual flow data and linear methods have limitations. Therefore, in this study, we used human and vehicle flow data to build and simulate the indices of mobile human activity. In addition, we used partial least squares regression (PLSR), random forest (RF), and geographical detector (GD) models to analyze the impact of mobile human activities on vegetation change. The results showed that indices of mobile human and vehicle flow increased by 1.43 and 7.68 times from 2000 to 2019 in the GBA, respectively. Simultaneously, vegetation increased by approximately 64%, whereas vegetation decreased mainly in the urban areas of the GBA. Vegetation change had no significant linear correlation with mobile human activities, exhibiting a regression coefficient below 0.1 and a weight of coefficients of PLSR less than 40 between vegetation change and all the factors of human activities. However, a more significant nonlinear relationship between vegetation change and driving factors were obtained. In the RF regression model, vegetation decrease was significantly affected by mobile human activity of vehicle flow, with an importance score of 108.11. From the GD method, vegetation decrease was found to mainly interact with indices of mobile human and vehicle inflow, and the highest interaction force was 0.82. These results may support the attainment of sustainable social–ecological systems and global environmental change.

Suggested Citation

  • Qionghuan Liu & Renzhong Guo & Zhengdong Huang & Biao He & Xiaoming Li, 2023. "The Nonlinear Impact of Mobile Human Activities on Vegetation Change in the Guangdong–Hong Kong–Macao Greater Bay Area," IJERPH, MDPI, vol. 20(3), pages 1-20, January.
    • Handle: RePEc:gam:jijerp:v:20:y:2023:i:3:p:1874-:d:1041558
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    References listed on IDEAS

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    1. Hao, Yu & Liu, Yiming & Weng, Jia-Hsi & Gao, Yixuan, 2016. "Does the Environmental Kuznets Curve for coal consumption in China exist? New evidence from spatial econometric analysis," Energy, Elsevier, vol. 114(C), pages 1214-1223.
    2. Yuyu Zhou, 2022. "Understanding urban plant phenology for sustainable cities and planet," Nature Climate Change, Nature, vol. 12(4), pages 302-304, April.
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