IDEAS home Printed from https://ideas.repec.org/a/gam/jlands/v11y2022i2p275-d746874.html
   My bibliography  Save this article

Influence of Urban Agglomeration Expansion on Fragmentation of Green Space: A Case Study of Beijing-Tianjin-Hebei Urban Agglomeration

Author

Listed:
  • Mingruo Chu

    (State Key Laboratory of Regional Sustainable Development Analysis and Simulation, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
    University of Chinese Academy of Sciences, Beijing 100049, China)

  • Jiayi Lu

    (State Key Laboratory of Regional Sustainable Development Analysis and Simulation, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
    University of Chinese Academy of Sciences, Beijing 100049, China)

  • Dongqi Sun

    (State Key Laboratory of Regional Sustainable Development Analysis and Simulation, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China)

Abstract

Loss of green space habitats and landscape fragmentation are important reasons for the decline in environmental quality, degradation of ecosystem functions, and decline in biodiversity. Quantifying the spatio-temporal characteristics of landscape fragmentation of green space and its relationship with urban expansion mode is an important basis for improving urban development mode and enhancing urban ecological functions. For this paper, we took the Beijing–Tianjin–Hebei (BTH) urban agglomeration as the research object, a typical rapidly urbanizing area. Through multi-scale landscape pattern analysis and statistical analysis, the spatial–temporal evolution characteristics of green space fragmentation in the BTH urban agglomeration from 2000 to 2020 and the influence of urban expansion were analyzed, and the land-use situation in 2030 was predicted by the Future Land Use Simulation (FLUS) model. The main conclusions are as follows: The BTH urban agglomeration has developed rapidly in the last 20 years, showing the characteristics of diffusion and corridor development. The intensity and pattern of urban expansion have significantly affected the pattern of green space, leading to the intensification of domestic green space fragmentation. Among them, urban expansion exerts most severe effects on the fragmentation of farmland, followed by grassland and water. The influence of urban expansion on the scale and fragmentation of forestland is limited. The forecast results in 2030 show that built-up areas may continue to occupy green space. The rate of occupation of farmland will slow down while that of grassland will intensify.

Suggested Citation

  • Mingruo Chu & Jiayi Lu & Dongqi Sun, 2022. "Influence of Urban Agglomeration Expansion on Fragmentation of Green Space: A Case Study of Beijing-Tianjin-Hebei Urban Agglomeration," Land, MDPI, vol. 11(2), pages 1-19, February.
  • Handle: RePEc:gam:jlands:v:11:y:2022:i:2:p:275-:d:746874
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2073-445X/11/2/275/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2073-445X/11/2/275/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Deng, Xiangzheng & Huang, Jikun & Rozelle, Scott & Uchida, Emi, 2008. "Growth, population and industrialization, and urban land expansion of China," Journal of Urban Economics, Elsevier, vol. 63(1), pages 96-115, January.
    2. R White & G Engelen, 1993. "Cellular Automata and Fractal Urban Form: A Cellular Modelling Approach to the Evolution of Urban Land-Use Patterns," Environment and Planning A, , vol. 25(8), pages 1175-1199, August.
    3. Tian, Guangjin & Ouyang, Yun & Quan, Quan & Wu, Jianguo, 2011. "Simulating spatiotemporal dynamics of urbanization with multi-agent systems—A case study of the Phoenix metropolitan region, USA," Ecological Modelling, Elsevier, vol. 222(5), pages 1129-1138.
    4. Tian, Guangjin & Qiao, Zhi & Zhang, Yaoqi, 2012. "The investigation of relationship between rural settlement density, size, spatial distribution and its geophysical parameters of China using Landsat TM images," Ecological Modelling, Elsevier, vol. 231(C), pages 25-36.
    5. Magliocca, Nicholas & McConnell, Virginia & Walls, Margaret, 2015. "Exploring sprawl: Results from an economic agent-based model of land and housing markets," Ecological Economics, Elsevier, vol. 113(C), pages 114-125.
    6. Xin Zhang & Jinghu Pan, 2021. "Spatiotemporal Pattern and Driving Factors of Urban Sprawl in China," Land, MDPI, vol. 10(11), pages 1-16, November.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Xiang Li & Zhaoshun Liu & Shujie Li & Yingxue Li, 2022. "Multi-Scenario Simulation Analysis of Land Use Impacts on Habitat Quality in Tianjin Based on the PLUS Model Coupled with the InVEST Model," Sustainability, MDPI, vol. 14(11), pages 1-18, June.
    2. Luhui Qi & Liqi Jia & Yubin Luo & Yuanyi Chen & Minggang Peng, 2022. "The External Characteristics and Mechanism of Urban Road Corridors to Agglomeration: Case Study for Guangzhou, China," Land, MDPI, vol. 11(7), pages 1-17, July.
    3. Yingting He & Chuyu Xia & Zhuang Shao & Jing Zhao, 2022. "The Spatiotemporal Evolution and Prediction of Carbon Storage: A Case Study of Urban Agglomeration in China’s Beijing-Tianjin-Hebei Region," Land, MDPI, vol. 11(6), pages 1-25, June.
    4. Bo Li & Yue Wang & Tong Wang & Xiaoman He & Jan K. Kazak, 2022. "Scenario Analysis for Resilient Urban Green Infrastructure," Land, MDPI, vol. 11(9), pages 1-19, September.
    5. Fangzhi Zhan & Zhicheng Liu & Boya Wang, 2023. "Study on the Spatiotemporal Evolution of the “Contraction–Expansion” Change of the Boundary Area between Two Green Belts in Beijing Based on a Multi-Index System," Land, MDPI, vol. 12(8), pages 1-21, August.
    6. Xuebin Zhang & Litang Yao & Jun Luo & Wenjuan Liang, 2022. "Exploring Changes in Land Use and Landscape Ecological Risk in Key Regions of the Belt and Road Initiative Countries," Land, MDPI, vol. 11(6), pages 1-22, June.
    7. Liang, Fachao & Zhu, Runmiao & Lin, Sheng-Hau, 2023. "Exploring spatial relationship between landscape configuration and ecosystem services: A case study of Xiamen–Zhangzhou–Quanzhou in China," Ecological Modelling, Elsevier, vol. 486(C).
    8. Wen Zhou & Yantao Xi & Liang Zhai & Cheng Li & Jingyang Li & Wei Hou, 2023. "Zoning for Spatial Conservation and Restoration Based on Ecosystem Services in Highly Urbanized Region: A Case Study in Beijing-Tianjin-Hebei, China," Land, MDPI, vol. 12(4), pages 1-15, March.
    9. Hao Li & Hongyu Chen & Minghao Wu & Kai Zhou & Xiang Zhang & Zhicheng Liu, 2022. "A Dynamic Evaluation Method of Urban Ecological Networks Combining Graphab and the FLUS Model," Land, MDPI, vol. 11(12), pages 1-15, December.
    10. Jingeng Huo & Zhenqin Shi & Wenbo Zhu & Tianqi Li & Hua Xue & Xin Chen & Yanhui Yan & Ran Ma, 2022. "Construction and Optimization of an Ecological Network in Zhengzhou Metropolitan Area, China," IJERPH, MDPI, vol. 19(13), pages 1-20, June.
    11. Hao Ye & Yongyong Song & Dongqian Xue, 2022. "Multi-Scenario Simulation of Land Use and Habitat Quality in the Guanzhong Plain Urban Agglomeration, China," IJERPH, MDPI, vol. 19(14), pages 1-22, July.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Guanglong Dong & Erqi Xu & Hongqi Zhang, 2015. "Spatiotemporal Variation of Driving Forces for Settlement Expansion in Different Types of Counties," Sustainability, MDPI, vol. 8(1), pages 1-17, December.
    2. Liu, Dongya & Zheng, Xinqi & Wang, Hongbin, 2020. "Land-use Simulation and Decision-Support system (LandSDS): Seamlessly integrating system dynamics, agent-based model, and cellular automata," Ecological Modelling, Elsevier, vol. 417(C).
    3. Nij Tontisirin & Sutee Anantsuksomsri, 2021. "Economic Development Policies and Land Use Changes in Thailand: From the Eastern Seaboard to the Eastern Economic Corridor," Sustainability, MDPI, vol. 13(11), pages 1-20, May.
    4. Yang, Xin & Zheng, Xin-Qi & Lv, Li-Na, 2012. "A spatiotemporal model of land use change based on ant colony optimization, Markov chain and cellular automata," Ecological Modelling, Elsevier, vol. 233(C), pages 11-19.
    5. Yaolin Liu & Xuesong Kong & Yanfang Liu & Yiyun Chen, 2013. "Simulating the Conversion of Rural Settlements to Town Land Based on Multi-Agent Systems and Cellular Automata," PLOS ONE, Public Library of Science, vol. 8(11), pages 1-14, November.
    6. Jinyao Lin & Tongli Chen & Qiazi Han, 2018. "Simulating and Predicting the Impacts of Light Rail Transit Systems on Urban Land Use by Using Cellular Automata: A Case Study of Dongguan, China," Sustainability, MDPI, vol. 10(4), pages 1-16, April.
    7. Xu Yang & Xuan Zou & Xueqi Liu & Qixuan Li & Siqian Zou & Ming Li, 2023. "The Spatiotemporal Pattern and Driving Mechanism of Urban Sprawl in China’s Counties," Land, MDPI, vol. 12(3), pages 1-16, March.
    8. Kaiming Li & Min Wang & Wenbin Hou & Fuyuan Gao & Baicui Xu & Jianjun Zeng & Dongyu Jia & Jun Li, 2023. "Spatial Distribution and Driving Mechanisms of Rural Settlements in the Shiyang River Basin, Western China," Sustainability, MDPI, vol. 15(16), pages 1-22, August.
    9. Kwok Hung Lau & Booi Hon Kam, 2005. "A Cellular Automata Model for Urban Land-Use Simulation," Environment and Planning B, , vol. 32(2), pages 247-263, April.
    10. Haiwen Zhou, 2013. "The Choice of Technology and Rural-Urban Migration in Economic Development," Frontiers of Economics in China-Selected Publications from Chinese Universities, Higher Education Press, vol. 8(3), pages 337-361, September.
    11. José I Barredo & Luca Demicheli & Carlo Lavalle & Marjo Kasanko & Niall McCormick, 2004. "Modelling Future Urban Scenarios in Developing Countries: An Application Case Study in Lagos, Nigeria," Environment and Planning B, , vol. 31(1), pages 65-84, February.
    12. Ortuño-Padilla, Armando & Fernández-Aracil, Patricia, 2013. "Impact of fuel price on the development of the urban sprawl in Spain," Journal of Transport Geography, Elsevier, vol. 33(C), pages 180-187.
    13. Yue Peng & Hui Qiu & Xinlu Wang, 2023. "The Influence of Spatial Functions on the Public Space System of Traditional Settlements," Sustainability, MDPI, vol. 15(11), pages 1-26, May.
    14. Dai, Jiangyu & Wu, Shiqiang & Han, Guoyi & Weinberg, Josh & Xie, Xinghua & Wu, Xiufeng & Song, Xingqiang & Jia, Benyou & Xue, Wanyun & Yang, Qianqian, 2018. "Water-energy nexus: A review of methods and tools for macro-assessment," Applied Energy, Elsevier, vol. 210(C), pages 393-408.
    15. Caruso, Geoffrey & Peeters, Dominique & Cavailhes, Jean & Rounsevell, Mark, 2007. "Spatial configurations in a periurban city. A cellular automata-based microeconomic model," Regional Science and Urban Economics, Elsevier, vol. 37(5), pages 542-567, September.
    16. Xu, Tingting & Gao, Jay & Li, Yuhua, 2019. "Machine learning-assisted evaluation of land use policies and plans in a rapidly urbanizing district in Chongqing, China," Land Use Policy, Elsevier, vol. 87(C).
    17. Long, Fenjie & Zheng, Longfei & Song, Zhida, 2018. "High-speed rail and urban expansion: An empirical study using a time series of nighttime light satellite data in China," Journal of Transport Geography, Elsevier, vol. 72(C), pages 106-118.
    18. Andrew Allan & Ali Soltani & Mohammad Hamed Abdi & Melika Zarei, 2022. "Driving Forces behind Land Use and Land Cover Change: A Systematic and Bibliometric Review," Land, MDPI, vol. 11(8), pages 1-20, August.
    19. Liu, Tie-Ying & Su, Chi-Wei, 2021. "Is transportation improving urbanization in China?," Socio-Economic Planning Sciences, Elsevier, vol. 77(C).
    20. Salvati, Luca & Sateriano, Adele & Grigoriadis, Efstathios & Carlucci, Margherita, 2017. "New wine in old bottles: The (changing) socioeconomic attributes of sprawl during building boom and stagnation," Ecological Economics, Elsevier, vol. 131(C), pages 361-372.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jlands:v:11:y:2022:i:2:p:275-:d:746874. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.