IDEAS home Printed from https://ideas.repec.org/a/spr/endesu/v26y2024i12d10.1007_s10668-023-04428-x.html
   My bibliography  Save this article

Evaluation and influencing factors of resources and environment carrying capacity of Guangdong–Hong Kong–Macao Greater Bay Area Economic Belt

Author

Listed:
  • Haiqi Zhang

    (Party School of the Central Committee of C.P.C (National Academy of Governance))

  • Guangqing Zhang

    (Shandong University
    Shandong University)

Abstract

The carrying capacity of urban resources and environment is an important yardstick to measure the sustainable development of a city, and it is also an important indicator to measure the degree of synergy between urban economic development and the environment. The main objective of this paper is to evaluate the economic, social, resource and environmental system carrying capacity of the cities in the Guangdong–Hong Kong–Macao Greater Bay Area (GBA) Economic Belt by building a state space model, so as to lay a theoretical foundation for promoting the green and sustainable development of the urban agglomeration in the GBA. The following conclusions were reached: (1) The resource and environmental carrying capacity of the 11 cities in the Guangdong–Hong Kong–Macao Greater Bay Area is generally overloaded and shows an "N" distribution pattern. (2) The level of the city's opening to the outside world, the government's management ability, the investment of science and technology funds, and the industrial development are the main factors that affect the resource and environmental carrying capacity of the urban agglomeration. (3) The regression analysis results of the economic and social subsystems and resource subsystems of the 11 cities in the Guangdong–Hong Kong–Macao Greater Bay Area are consistent with the results of the overall system of resource and environmental carrying capacity. The spatial differentiation characteristics are weakened, but the spatial differentiation characteristics of the resource and environmental carrying capacity of the environmental subsystem are gradually obvious. The innovation of this paper is that we can see the state of the resource and environment carrying capacity of the urban agglomeration in the Guangdong–Hong Kong–Macao Greater Bay Area by building a state space model. Using GIS geographic mapping technology, we can clearly see the dynamic changes of urban agglomeration during the 10-year period and realize the unification of space–time evolution.

Suggested Citation

  • Haiqi Zhang & Guangqing Zhang, 2024. "Evaluation and influencing factors of resources and environment carrying capacity of Guangdong–Hong Kong–Macao Greater Bay Area Economic Belt," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 26(12), pages 30221-30237, December.
    • Handle: RePEc:spr:endesu:v:26:y:2024:i:12:d:10.1007_s10668-023-04428-x
    DOI: 10.1007/s10668-023-04428-x
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10668-023-04428-x
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s10668-023-04428-x?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Yen-Chang Chang & Lii-Yuh Leu, 1998. "A State Space Model for Software Reliability," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 50(4), pages 789-799, December.
    2. Chao Bao & Chuang-lin Fang, 2012. "Water Resources Flows Related to Urbanization in China: Challenges and Perspectives for Water Management and Urban Development," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 26(2), pages 531-552, January.
    3. Lu, Wen-Min & Lo, Shih-Fang, 2007. "A closer look at the economic-environmental disparities for regional development in China," European Journal of Operational Research, Elsevier, vol. 183(2), pages 882-894, December.
    4. Zhou, Ya & Shan, Yuli & Liu, Guosheng & Guan, Dabo, 2018. "Emissions and low-carbon development in Guangdong-Hong Kong-Macao Greater Bay Area cities and their surroundings," Applied Energy, Elsevier, vol. 228(C), pages 1683-1692.
    Full references (including those not matched with items on IDEAS)

    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. Salvati, Luca & Carlucci, Margherita, 2011. "The economic and environmental performances of rural districts in Italy: Are competitiveness and sustainability compatible targets?," Ecological Economics, Elsevier, vol. 70(12), pages 2446-2453.
    2. Jinzhao Song & Qing Feng & Xiaoping Wang & Hanliang Fu & Wei Jiang & Baiyu Chen, 2018. "Spatial Association and Effect Evaluation of CO 2 Emission in the Chengdu-Chongqing Urban Agglomeration: Quantitative Evidence from Social Network Analysis," Sustainability, MDPI, vol. 11(1), pages 1-19, December.
    3. Li, Li & Shan, Yuli & Lei, Yalin & Wu, Sanmang & Yu, Xiang & Lin, Xiyan & Chen, Yupei, 2019. "Decoupling of economic growth and emissions in China’s cities: A case study of the Central Plains urban agglomeration," Applied Energy, Elsevier, vol. 244(C), pages 36-45.
    4. Junbo Wang & Liu Chen & Lu Chen & Xiaohui Zhao & Minxi Wang & Yiyi Ju & Li Xin, 2019. "City-Level Features of Energy Footprints and Carbon Dioxide Emissions in Sichuan Province of China," Energies, MDPI, vol. 12(10), pages 1-14, May.
    5. Chunli Zhou & Yuze Tang & Deyan Zhu & Zhiwei Cui, 2024. "Tracking the Carbon Emissions Using Electricity Big Data: A Case Study of the Metal Smelting Industry," Energies, MDPI, vol. 17(3), pages 1-19, January.
    6. Xiao, Huijuan & Wang, Daoping & Qi, Yu & Shao, Shuai & Zhou, Ya & Shan, Yuli, 2021. "The governance-production nexus of eco-efficiency in Chinese resource-based cities: A two-stage network DEA approach," Energy Economics, Elsevier, vol. 101(C).
    7. Ramli, Noor Asiah & Munisamy, Susila, 2015. "Eco-efficiency in greenhouse emissions among manufacturing industries: A range adjusted measure," Economic Modelling, Elsevier, vol. 47(C), pages 219-227.
    8. Dawei Wen & Song Ma & Anlu Zhang & Xinli Ke, 2021. "Spatial Pattern Analysis of the Ecosystem Services in the Guangdong-Hong Kong-Macao Greater Bay Area Using Sentinel-1 and Sentinel-2 Imagery Based on Deep Learning Method," Sustainability, MDPI, vol. 13(13), pages 1-16, June.
    9. Pereira, Deivison da Silveira & Soares de Mello, João Carlos C.B., 2021. "Efficiency evaluation of Brazilian airlines operations considering the Covid-19 outbreak," Journal of Air Transport Management, Elsevier, vol. 91(C).
    10. Haifeng Yang & Guofang Zhai & Yifu Ge & Tong Jiang & Buda Su, 2025. "Spatial–Temporal Difference of Urban Carbon Budget and Carbon Compensation Optimization Partition from the Perspective of Spatial Planning," Land, MDPI, vol. 14(2), pages 1-19, February.
    11. Rong Sheng & Shi Lin Liu, 2018. "Evolution on regional disparities in China from 2000 to 2012: evidence from 74 cities within three mega-urban regions along Yangtze River," Quality & Quantity: International Journal of Methodology, Springer, vol. 52(5), pages 1989-2006, September.
    12. Tsai, Wen-Ping & Cheng, Chung-Lien & Uen, Tinn-Shuan & Zhou, Yanlai & Chang, Fi-John, 2019. "Drought mitigation under urbanization through an intelligent water allocation system," Agricultural Water Management, Elsevier, vol. 213(C), pages 87-96.
    13. Qi Fu & Mengfan Gao & Yue Wang & Tinghui Wang & Xu Bi & Jinhua Chen, 2022. "Spatiotemporal Patterns and Drivers of the Carbon Budget in the Yangtze River Delta Region, China," Land, MDPI, vol. 11(8), pages 1-18, August.
    14. Wenwei Hou & Fan Liu & Yanqin Zhang & Jiaying Dong & Shumeng Lin & Minhua Wang, 2024. "Research Progress and Hotspot Analysis of Low-Carbon Landscapes Based on CiteSpace Analysis," Sustainability, MDPI, vol. 16(17), pages 1-24, September.
    15. B. Sarma & A. Sarma & V. Singh, 2013. "Optimal Ecological Management Practices (EMPs) for Minimizing the Impact of Climate Change and Watershed Degradation Due to Urbanization," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 27(11), pages 4069-4082, September.
    16. Tavana, Madjid & Ebrahimnejad, Ali & Santos-Arteaga, Francisco J. & Mansourzadeh, Seyed Mehdi & Matin, Reza Kazemi, 2018. "A hybrid DEA-MOLP model for public school assessment and closure decision in the City of Philadelphia," Socio-Economic Planning Sciences, Elsevier, vol. 61(C), pages 70-89.
    17. Chen, Mengting & Linker, Raphael & Wu, Conglin & Xie, Hua & Cui, Yuanlai & Luo, Yufeng & Lv, Xinwei & Zheng, Shizong, 2022. "Multi-objective optimization of rice irrigation modes using ACOP-Rice model and historical meteorological data," Agricultural Water Management, Elsevier, vol. 272(C).
    18. Shiang-Tai Liu & Yueh-Chiang Lee, 2021. "Fuzzy measures for fuzzy cross efficiency in data envelopment analysis," Annals of Operations Research, Springer, vol. 300(2), pages 369-398, May.
    19. Carayannis, Elias G. & Grigoroudis, Evangelos & Wurth, Bernd, 2022. "OR for entrepreneurial ecosystems: A problem-oriented review and agenda," European Journal of Operational Research, Elsevier, vol. 300(3), pages 791-808.
    20. Wu, Feng & Zhan, Jinyan & Güneralp, İnci, 2015. "Present and future of urban water balance in the rapidly urbanizing Heihe River Basin, Northwest China," Ecological Modelling, Elsevier, vol. 318(C), pages 254-264.

    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:spr:endesu:v:26:y:2024:i:12:d:10.1007_s10668-023-04428-x. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.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.