Exploration of Urban Subsystem Coupling Coordination Based on Resilience in Luohe City

Authors

  • Wang Xinyu Hungarian University of Agriculture and Life Sciences, Institute of Landscape Architecture, Urban Planning and Garden Art, Budapest, Department of Landscape Planning and Regional Development
  • Shi Zhen Hungarian University of Agriculture and Life Sciences, Institute of Landscape Architecture, Urban Planning and Garden Art, Budapest, Department of Landscape Planning and Regional Development
  • László Kollányi Hungarian University of Agriculture and Life Sciences, Institute of Landscape Architecture, Urban Planning and Garden Art, Budapest, Department of Landscape Planning and Regional Development
  • Yang Yang Hungarian University of Agriculture and Life Sciences, Institute of Landscape Architecture, Urban Planning and Garden Art, Budapest, Department of Landscape Planning and Regional Development
  • Liu Manshu Hungarian University of Agriculture and Life Sciences, Institute of Landscape Architecture, Urban Planning and Garden Art, Budapest, Department of Landscape Planning and Regional Development
  • Zhang Xiaoyan ELTE Eötvös Loránd University, Faculty of Science/Doctoral School of Earth Sciences

DOI:

https://doi.org/10.36249/4d.67.3700

Keywords:

Urban Resilience, Society-Economy-Ecology, Coupling Coordination Degree, ECO Model, Systems Theory

Abstract

With urbanization, the uncertainties faced by urban areas continue to increase, and in response, China's urban planning is transitioning from a focus on quantity to quality. Promoting system coupling and coordination helps to make urban areas more resilient to risk. This paper uses data from Luohe City (China) in 2022 as an example to calculate the inter-system coupling coordination degree (CCD), find the factors that obstruct resilience construction, and further explore the spatial heterogeneity of the obstacle factors. The synthetic evaluation model, coupling evaluation model, and obstacle diagnosis model are used to evaluate and analyze each subsystem. The results are as follows: According to the synthetic evaluation, the mean urban development value is 0.48, with high-value regions clustered in the Southeastern built-up zone. Mean value of urban coupling coordination is 0.66, the coordination level is 7 (Moderate Coordination). Urban development degree is positively correlated with CCD. Globally, the economy is the main factor obstructing resilient growth, but the core obstacle areas of the ecosystem are larger and have a wider impact. This study contributes to understanding the internal action of urban systems and provides data for balance development and urban resilience enhancement.

 

Author Biographies

  • Wang Xinyu, Hungarian University of Agriculture and Life Sciences, Institute of Landscape Architecture, Urban Planning and Garden Art, Budapest, Department of Landscape Planning and Regional Development

    PhD student
    e-mail: sdwangxinyuxw@126.com

  • Shi Zhen, Hungarian University of Agriculture and Life Sciences, Institute of Landscape Architecture, Urban Planning and Garden Art, Budapest, Department of Landscape Planning and Regional Development

    PhD student
    e-mail: Shi.Zhen@PhD.uni-mate.hu

  • László Kollányi, Hungarian University of Agriculture and Life Sciences, Institute of Landscape Architecture, Urban Planning and Garden Art, Budapest, Department of Landscape Planning and Regional Development

    associate professor, CSc
    e-mail: kollanyi.laszlo@uni-mate.hu

  • Yang Yang, Hungarian University of Agriculture and Life Sciences, Institute of Landscape Architecture, Urban Planning and Garden Art, Budapest, Department of Landscape Planning and Regional Development

    PhD student
    e-mail: Yang.Yang@phd.uni-mate.hu

  • Liu Manshu, Hungarian University of Agriculture and Life Sciences, Institute of Landscape Architecture, Urban Planning and Garden Art, Budapest, Department of Landscape Planning and Regional Development

    PhD student
    e-mail: lms_1002@163.com

  • Zhang Xiaoyan, ELTE Eötvös Loránd University, Faculty of Science/Doctoral School of Earth Sciences

    PhD student
    e-mail: zxy@student.elte.hu

References

Ribeiro, P. J. G., & Gonçalves, L. A. P. J. (2019). Urban resilience: A conceptual framework. SCS, 50, 101625. https://doi.org/10.1016/j.scs.2019.101625.

Lhomme, S., Serre, D., Diab, Y., & Laganier, R. (2012). Urban technical networks resilience assessment. In D. Serre, B. Barroca, & R. Laganier (Eds.), Resilience and Urban Risk Management (pp. 109–117). CRC Press.

Batty, M. (2002). Thinking about cities as spatial events. Environment and Planning B: Planning and Design, 29, 1–2. https://doi.org/10.1068/b2901ed.

Büyüközkan, G., Ilıcak, Ö., & Feyzioğlu, O. (2022). A review of urban resilience literature. SCS, 77, 103579. https://doi.org/10.1016/j.scs.2021.103579.

City Resilience Index. (2014). City resilience framework. The Rockefeller Foundation and ARUP.

Meerow, S., Newell, J. P., & Stults, M. (2016). Defining urban resilience: A review. Landscape and Urban Planning, 147, 38–49. https://doi.org/10.1016/j.landurbplan.2015.11.011.

Assumma, V., Bottero, M., Datola, G., Pezzoli, A., & Quagliolo, C. (2021). Climate change and urban resilience. Preliminary insights from an integrated evaluation framework. In C. Bevilacqua, F. Calabrò, & L. D. (Eds.), International Symposium: New Metropolitan Perspectives (pp. 676–685). Springer International Publishing.

Masnavi, M. R., Gharai, F., & Hajibandeh, M. (2019). Exploring urban resilience thinking for its application in urban planning: A review of literature. International Journal of Environmental Science and Technology, 16, 567–582. https://doi.org/10.1007/s13762-018-1860-2.

Panahi, R., Gargari, N. S., Lau, Y., & Ng, A. K. (2022). Developing a resilience assessment model for critical infrastructures: The case of port in tackling the impacts posed by the Covid-19 pandemic. Ocean & Coastal Management, 226, 106240. https://doi.org/10.1016/j.ocecoaman.2022.106240.

Shutters, S. T., Muneepeerakul, R., & Lobo, J. (2015). Quantifying urban economic resilience through labour force interdependence. Palgrave Communications, 1, 1–7. https://doi.org/10.1057/palcomms.2015.10.

Wang, X., Yao, X., Shao, H., Bai, T., Xu, Y., Tian, G., Fekete, A., & Kollányi, L. (2023). Land use quality assessment and exploration of the driving forces based on location: A case study in Luohe City, China. Land, 12, 1–17. https://doi.org/10.3390/land12010257.

Cui, X., Yang, S., Zhang, G., Liang, B., & Li, F. (2020). An exploration of a synthetic construction land use quality evaluation based on economic-social-ecological coupling perspective: A case study in major Chinese cities. International Journal of Environmental Research and Public Health, 17(10). https://doi.org/10.3390/ijerph17103663.

Dong, L., Longwu, L., Zhenbo, W., Liangkan, C., & Faming, Z. (2021). Exploration of coupling effects in the Economy–Society–Environment system in urban areas: Case study of the Yangtze River Delta Urban Agglomeration. Ecological Indicators, 128, 107858. https://doi.org/10.1016/j.ecolind.2021.107858.

Yang, L. (2019). Evaluating the urban land use plan with transit accessibility. SCS, 45, 474–485. https://doi.org/10.1016/j.scs.2018.11.042.

Ariken, M., Zhang, F., & Weng Chan, N. (2021). Coupling coordination analysis and spatio-temporal heterogeneity between urbanization and eco-environment along the Silk Road Economic Belt in China. Ecological Indicators, 121, 107014. https://doi.org/10.1016/j.ecolind.2020.107014.

Lin, Y., Peng, C., Chen, P., & Zhang, M. (2022). Conflict or synergy? Analysis of economic-social-infrastructure-ecological resilience and their coupling coordination in the Yangtze River Economic Belt, China. Ecological Indicators, 142, 109194. https://doi.org/10.1016/j.ecolind.2022.109194.

Tian, Y., Zhou, D., & Jiang, G. (2020). Conflict or coordination? Multiscale assessment of the spatio-temporal coupling relationship between urbanization and ecosystem services: The case of the Jingjinji Region, China. Ecological Indicators, 117, 106543. https://doi.org/10.1016/j.ecolind.2020.106543.

Liu, F., Zhang, Z., Shi, L., Zhao, X., Xu, J., Yi, L., Liu, B., Wen, Q., Hu, S., & Wang, X. (2016). Urban expansion in China and its spatial-temporal differences over the past four decades. Journal of Geographical Sciences, 26, 1477–1496. https://doi.org/10.1007/s11442-016-1339-3.

Wang, X., Dong, X., Liu, H., Wei, H., Fan, W., Lu, N., Xu, Z., Ren, J., & Xing, K. (2017). Linking land use change, ecosystem services and human well-being: A case study of the Manas River Basin of Xinjiang, China. Ecosystem Services, 27, 113–123. https://doi.org/10.1016/j.ecoser.2017.08.013.

Cong, X. (2019). Expression and mathematical property of coupling model, and its misuse in geographical science. Economic Geography, 39(4), 18–25.

Ermida, S. L., Soares, P., Mantas, V., Göttsche, F. M., & Trigo, I. F. (2020). Google earth engine open-source code for land surface temperature estimation from the landsat series. Remote Sensing, 12, 1471. https://doi.org/10.3390/rs12091471.

Li, Z., Yang, X. M., Meng, F., Chen, X., & Yang, F. S. (2017). The Method of Multi-source Remote Sensing Synergy Extraction in Urban Built-up Area. Journal of Geo-Information Science, 19, 1522. https://doi.org/10.3724/SP.J.1047.2017.01522.

Wang, X., Kollányi, L., Shi, Z., Liu, M., & Yang, Y. (2022). Study On Land Use Aggregation Pattern of Luohe City Based On Spatial Heterogeneity. Proceedings of the Fábos Conference on Landscape and Greenway Planning, 7(1), Article 56. https://doi.org/10.7275/0j3n-x734.

Luo, J., Zhou, C., Leung, Y., & Ma, J. (2002). Finite mixture model and its EM clustering algorithm for remote sensing data. Journal of Image and Graphic, 7, 336–340. https://doi.org/10.11834/jig.200204119.

Zhou, L., Dang, X., Sun, Q., & Wang, S. (2020). Multi-scenario simulation of urban land change in Shanghai by random forest and CA-Markov model. Sustainable Cities and Society, 55, 102045. https://doi.org/10.1016/j.scs.2020.102045.

Sheng, Q., Yang, T., & Hou, J. (2015). Continuous Movement and Hyper-link Spatial Mechanisms——A Large-scale Space Syntax Analysis on Chongqing’s Vehicle and Metro Flow Data. Journal of Human Settlements in West China, 30, 16–21. https://doi.org/10.13791/j.cnki.hsfwest.20150503.

Diakoulaki, D., Mavrotas, G., & Papayannakis, L. (1995). Determining objective weights in multiple criteria problems: The critic method. Computers & Operations Research, 22, 763–770. https://doi.org/10.1016/0305-0548(94)00059-h.

Downloads

Published

2023-04-30

Issue

Section

Articles

How to Cite

Exploration of Urban Subsystem Coupling Coordination Based on Resilience in Luohe City. (2023). 4D Journal of Landscape Architecture and Garden Art, 67, 22-29. https://doi.org/10.36249/4d.67.3700

Similar Articles

11-20 of 43

You may also start an advanced similarity search for this article.

Most read articles by the same author(s)