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Urban Expansion Was the Main Driving Force for the Decline in Ecosystem Services in Hainan Island during 1980–2015

Author

Listed:
  • Jia Geng

    (International Hospitality Management School, University of Sanya, Sanya 572000, China)

  • Mingsheng Yuan

    (School of Business Administration, Northeastern University, Shenyang 110819, China)

  • Shen Xu

    (School of Psychological and Cognitive Sciences, Peking University, Beijing 100871, China)

  • Tingting Bai

    (School of Business Administration, Northeastern University, Shenyang 110819, China)

  • Yang Xiao

    (Academician Workstation of Zhai Mingguo, University of Sanya, Sanya 572022, China)

  • Xiaopeng Li

    (The Third Engineering Co., Ltd. of China Railway 22nd Bureau Group, Xiamen 361000, China)

  • Dong Xu

    (State Key Laboratory of Remote Sensing Science, Beijing Normal University, Beijing 100091, China)

Abstract

Hainan Island is one of China’s most ecologically diverse areas. Human activities and climate change have recently influenced Hainan Island’s ecosystem services. Therefore, scientific methods are urgently needed to investigate the characteristics of these services’ spatial and temporal variations and their driving mechanisms for maintaining Hainan Island’s biodiversity and high-quality ecological conservation. Based on multivariate remote sensing and reanalysis data, this study analysed the spatial and temporal variations in water retention, soil conservation, carbon sequestration, and oxygen release services on Hainan Island during 1980–2015 using various ecosystem service models such as INVEST, CASA and RULSE. Then, we analysed different ecosystem service drivers using a random forest model. The results indicated that (1) from 1980 to 2015, the change characteristics of different ecosystem types (arable, forest, and grassland) decreased, and the proportion of decrease was 0.98%, 0.55% and 0.36%, respectively. Built-up and water increased significantly, and the proportion of increase reached 1.46% and 0.51%, respectively. (2) Hainan Island’s functions of water retention, soil conservation, carbon sequestration, and oxygen release services decreased from 23.31 billion m3, 2.89 billion t, 9.68 million t and 56.05 million t in 1980 to 23.15 billion m3, 2.79 billion t, 9.42 million t and 55.53 million t in 2015, respectively. The high value area was mainly distributed in Hainan Island’s central mountainous area, and the low value area was mainly distributed in the lower-elevation coastal area. (3) In the past 35 years, urban expansion has been the leading factor in the reduction of Hainan Island’s ecosystem service capacity. However, its central nature reserve and other forms of ecological protection have improved its ecosystem service capacity, which has alleviated the overall declining trend of its amount of ecosystem service functions. (4) The driving forces for the spatial distribution of Hainan Island’s ecosystem services were analysed using a random forest algorithm, which indicated that its spatial distribution was mainly driven by rainfall, soil moisture, actual evapotranspiration, maximum temperature, and minimum temperature. This study is expected to help planners develop effective environmental policies to accommodate the potential ecological risks associated with urban expansion during the construction of Hainan Island’s future free trade port while filling the gaps in existing studies.

Suggested Citation

  • Jia Geng & Mingsheng Yuan & Shen Xu & Tingting Bai & Yang Xiao & Xiaopeng Li & Dong Xu, 2022. "Urban Expansion Was the Main Driving Force for the Decline in Ecosystem Services in Hainan Island during 1980–2015," IJERPH, MDPI, vol. 19(23), pages 1-18, November.
    • Handle: RePEc:gam:jijerp:v:19:y:2022:i:23:p:15665-:d:983668
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    1. Archer, Kellie J. & Kimes, Ryan V., 2008. "Empirical characterization of random forest variable importance measures," Computational Statistics & Data Analysis, Elsevier, vol. 52(4), pages 2249-2260, January.
    2. Peng Tian & Luodan Cao & Jialin Li & Ruiliang Pu & Xiaoli Shi & Lijia Wang & Ruiqing Liu & Hao Xu & Chen Tong & Zijing Zhou & Shuyao Shao, 2019. "Landscape Grain Effect in Yancheng Coastal Wetland and Its Response to Landscape Changes," IJERPH, MDPI, vol. 16(12), pages 1-20, June.
    3. Hao Hu & Shufang Wang & Jin-liao He, 2020. "Comparative Advantages of Free Trade Port Construction in Shanghai under the Belt and Road Initiative," IJFS, MDPI, vol. 8(1), pages 1-15, February.
    4. Jose Dias Curto & Jose Castro Pinto, 2009. "The coefficient of variation asymptotic distribution in the case of non-iid random variables," Journal of Applied Statistics, Taylor & Francis Journals, vol. 36(1), pages 21-32.
    5. Zeng, Douglas Zhihua, 2011. "How do special economic zones and industrial clusters drive China's rapid development ?," Policy Research Working Paper Series 5583, The World Bank.
    6. Gang Lin & Dong Jiang & Jingying Fu & Yi Zhao, 2022. "A Review on the Overall Optimization of Production–Living–Ecological Space: Theoretical Basis and Conceptual Framework," Land, MDPI, vol. 11(3), pages 1-15, February.
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