IDEAS home Printed from https://ideas.repec.org/a/gam/jlands/v11y2022i6p858-d832999.html
   My bibliography  Save this article

The Spatiotemporal Evolution and Prediction of Carbon Storage: A Case Study of Urban Agglomeration in China’s Beijing-Tianjin-Hebei Region

Author

Listed:
  • Yingting He

    (School of Landscape Architecture, Beijing Forestry University, Beijing 100083, China)

  • Chuyu Xia

    (Faculty of Architecture, Civil and Transportation Engineering, Beijing University of Technology, Beijing 100124, China)

  • Zhuang Shao

    (School of Landscape Architecture, Beijing Forestry University, Beijing 100083, China)

  • Jing Zhao

    (School of Landscape Architecture, Beijing Forestry University, Beijing 100083, China)

Abstract

Due to rapid urban expansion, urban agglomerations face enormous challenges on their way to carbon neutrality. Regarding China’s urban agglomerations, 25% of the land contains 75% of the population, and all types of land are used efficiently and intensively. However, few studies have explored the spatiotemporal link between changes in land use and land cover (LULC) and carbon storage. In this work, the carbon storage changes from 1990 to 2020 were estimated using the InVEST model in China’s Beijing–Tianjin–Hebei (BTH) region. By coupling the Future Land Use Simulation (FLUS) model and InVEST model, the LULC and carbon storage changes in the BTH region in 2035 and 2050 under the natural evolution scenario (NES), economic priority scenario (EPS), ecological conservation scenario (ECS), and coordinated development scenario (CDS). Finally, the spatial autocorrelation analysis of regional carbon storage was developed for future zoning management. The results revealed the following: (1) the carbon storage in the BTH region exhibited a cumulative loss of 3.5 × 10 7 Mg from 1990 to 2020, and the carbon loss was serious between 2000 and 2010 due to rapid urbanization. (2) Excluding the ECS, the other three scenarios showed continued expansion of construction land. Under the EPS, the carbon storage was found to have the lowest value, which decreased to 16.05 × 10 8 Mg in 2035 and only 15.38 × 10 8 Mg in 2050; under the ECS, the carbon storage was predicted to reach the highest value, 18.22 × 10 8 Mg and 19.00 × 10 8 Mg, respectively; the CDS exhibited a similar trend as the NES, but the carbon storage was found to increase. (3) The carbon storage under the four scenarios was found to have a certain degree of similarity in terms of its spatial distribution; the high-value areas were found to be clustered in the northwestern part of Beijing and the northern and western parts of Hebei. As for the number of areas with high carbon storage, the ECS was found to be the most abundant, followed by the CDS, and the EPS was found to be the least. The findings of this study can help the BTH region implement the “dual carbon” target and provide a leading example for other urban agglomerations.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jlands:v:11:y:2022:i:6:p:858-:d:832999
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2073-445X/11/6/858/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2073-445X/11/6/858/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Wenbo Cai & Wanting Peng, 2021. "Exploring Spatiotemporal Variation of Carbon Storage Driven by Land Use Policy in the Yangtze River Delta Region," Land, MDPI, vol. 10(11), pages 1-12, October.
    2. Henghui Xi & Wanglai Cui & Li Cai & Mengyuan Chen & Chenglei Xu, 2021. "Evaluation and Prediction of Ecosystem Service Value in the Zhoushan Islands Based on LUCC," Sustainability, MDPI, vol. 13(4), pages 1-13, February.
    3. Jiang, Weiguo & Deng, Yue & Tang, Zhenghong & Lei, Xuan & Chen, Zheng, 2017. "Modelling the potential impacts of urban ecosystem changes on carbon storage under different scenarios by linking the CLUE-S and the InVEST models," Ecological Modelling, Elsevier, vol. 345(C), pages 30-40.
    4. 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.
    5. Qing Liu & Dongdong Yang & Lei Cao & Bruce Anderson, 2022. "Assessment and Prediction of Carbon Storage Based on Land Use/Land Cover Dynamics in the Tropics: A Case Study of Hainan Island, China," Land, MDPI, vol. 11(2), pages 1-24, February.
    6. Xia, Chuyu & Chen, Bin, 2020. "Urban land-carbon nexus based on ecological network analysis," Applied Energy, Elsevier, vol. 276(C).
    7. Pakawan Chotchaiwong & Saowanee Wijitkosum, 2019. "Predicting Urban Expansion and Urban Land Use Changes in Nakhon Ratchasima City Using a CA-Markov Model under Two Different Scenarios," Land, MDPI, vol. 8(9), pages 1-16, September.
    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. Tong Lin & Dafang Wu & Muzhuang Yang & Peifang Ma & Yanyan Liu & Feng Liu & Ziying Gan, 2022. "Evolution and Simulation of Terrestrial Ecosystem Carbon Storage and Sustainability Assessment in Karst Areas: A Case Study of Guizhou Province," IJERPH, MDPI, vol. 19(23), pages 1-19, December.
    2. Song Li & Fei Xue & Chuyu Xia & Jian Zhang & Ao Bian & Yuexi Lang & Jun Zhou, 2022. "A Big Data-Based Commuting Carbon Emissions Accounting Method—A Case of Hangzhou," Land, MDPI, vol. 11(6), pages 1-18, June.
    3. Lili Geng & Yuanyuan Zhang & Huixian Hui & Yuhan Wang & Yongji Xue, 2023. "Response of Urban Ecosystem Carbon Storage to Land Use/Cover Change and Its Vulnerability Based on Major Function-Oriented Zone Planning," Land, MDPI, vol. 12(8), pages 1-21, August.
    4. Yonghua Li & Song Yao & Hezhou Jiang & Huarong Wang & Qinchuan Ran & Xinyun Gao & Xinyi Ding & Dandong Ge, 2022. "Spatial-Temporal Evolution and Prediction of Carbon Storage: An Integrated Framework Based on the MOP–PLUS–InVEST Model and an Applied Case Study in Hangzhou, East China," Land, MDPI, vol. 11(12), pages 1-22, December.

    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. Yonghua Li & Song Yao & Hezhou Jiang & Huarong Wang & Qinchuan Ran & Xinyun Gao & Xinyi Ding & Dandong Ge, 2022. "Spatial-Temporal Evolution and Prediction of Carbon Storage: An Integrated Framework Based on the MOP–PLUS–InVEST Model and an Applied Case Study in Hangzhou, East China," Land, MDPI, vol. 11(12), pages 1-22, December.
    2. Yang Chen & Wenze Yue & Xue Liu & Linlin Zhang & Ye’an Chen, 2021. "Multi-Scenario Simulation for the Consequence of Urban Expansion on Carbon Storage: A Comparative Study in Central Asian Republics," Land, MDPI, vol. 10(6), pages 1-17, June.
    3. Qiangqiang Yang & Pian Zhang & Xiaocong Qiu & Guanglai Xu & Jianyu Chi, 2023. "Spatial-Temporal Variations and Trade-Offs of Ecosystem Services in Anhui Province, China," IJERPH, MDPI, vol. 20(1), pages 1-18, January.
    4. Chen Qu & Wen Li & Jia Xu & Song Shi, 2023. "Blackland Conservation and Utilization, Carbon Storage and Ecological Risk in Green Space: A Case Study from Heilongjiang Province in China," IJERPH, MDPI, vol. 20(4), pages 1-21, February.
    5. Qing Liu & Dongdong Yang & Lei Cao & Bruce Anderson, 2022. "Assessment and Prediction of Carbon Storage Based on Land Use/Land Cover Dynamics in the Tropics: A Case Study of Hainan Island, China," Land, MDPI, vol. 11(2), pages 1-24, February.
    6. Xinhui Feng & Yan Li & Lu Zhang & Chuyu Xia & Er Yu & Jiayu Yang, 2022. "Carbon Metabolism in Urban “Production–Living–Ecological” Space Based on Ecological Network Analysis," Land, MDPI, vol. 11(9), pages 1-22, August.
    7. Zhiyuan Ma & Xuejun Duan & Lei Wang & Yazhu Wang & Jiayu Kang & Ruxian Yun, 2023. "A Scenario Simulation Study on the Impact of Urban Expansion on Terrestrial Carbon Storage in the Yangtze River Delta, China," Land, MDPI, vol. 12(2), pages 1-16, January.
    8. Kukkonen, M.O. & Khamis, M. & Muhammad, M.J. & Käyhkö, N. & Luoto, M., 2022. "Modeling direct above-ground carbon loss due to urban expansion in Zanzibar City Region, Tanzania," Land Use Policy, Elsevier, vol. 112(C).
    9. 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.
    10. Qing Lu & Jing Ning & Hong You & Liyan Xu, 2023. "Urban Intensity in Theory and Practice: Empirical Determining Mechanism of Floor Area Ratio and Its Deviation from the Classic Location Theories in Beijing," Land, MDPI, vol. 12(2), pages 1-16, February.
    11. 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.
    12. Xiang Pan & Peiji Shi & Na Wu, 2020. "Spatial–Temporal Interaction Relationship between Ecosystem Services and Urbanization of Urban Agglomerations in the Transitional Zone of Three Natural Regions," Sustainability, MDPI, vol. 12(23), pages 1-14, December.
    13. Xinyu Ouyang & Xiangyu Luo, 2022. "Models for Assessing Urban Ecosystem Services: Status and Outlooks," Sustainability, MDPI, vol. 14(8), pages 1-20, April.
    14. Jinfeng Wang & Ya Li & Sheng Wang & Qing Li & Lingfeng Li & Xiaoling Liu, 2023. "Assessment of Multiple Ecosystem Services and Ecological Security Pattern in Shanxi Province, China," IJERPH, MDPI, vol. 20(6), pages 1-18, March.
    15. Jincai Zhao & Qianqian Liu, 2021. "Examining the Driving Factors of Urban Residential Carbon Intensity Using the LMDI Method: Evidence from China’s County-Level Cities," IJERPH, MDPI, vol. 18(8), pages 1-18, April.
    16. Yongchao Qu & Jian Zhang & Chongyuan Xu & Yichao Gao & Shanwen Zheng & Meiling Xia, 2022. "Analysis of Spatial Carbon Metabolism by ENA: A Case Study of Tongzhou District, Beijing," Land, MDPI, vol. 11(9), pages 1-17, September.
    17. Haiyan Meng & Yi Hu & Zuoji Dong, 2023. "Landscape Pattern Change and Ecological Effect in a Typical Mountain–Oasis–Desert Region in the Northwest Region of China," IJERPH, MDPI, vol. 20(5), pages 1-19, February.
    18. 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.
    19. Tiecheng Huang & Wenjiang Huang & Kun Wang & Yongkang Li & Zhenhai Li & Yong’an Yang, 2022. "Ecosystem Service Value Estimation of Paddy Field Ecosystems Based on Multi-Source Remote Sensing Data," Sustainability, MDPI, vol. 14(15), pages 1-22, August.
    20. Wenyi Qiao & Weihua Guan & Xianjin Huang, 2021. "Assessing the Potential Impact of Land Use on Carbon Storage Driven by Economic Growth: A Case Study in Yangtze River Delta Urban Agglomeration," IJERPH, MDPI, vol. 18(22), pages 1-20, November.

    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:6:p:858-:d:832999. 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.