IDEAS home Printed from https://ideas.repec.org/a/gam/jijerp/v18y2021i19p10501-d650895.html
   My bibliography  Save this article

Scenario-Based Analysis of Land Use Competition and Sustainable Land Development in Zhangye of the Heihe River Basin, China

Author

Listed:
  • Yuping Bai

    (School of Land Science and Technology, China University of Geosciences, Beijing 100083, China
    Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China)

  • Zhe Zhao

    (School of Economics, Liaoning University, Shenyang 110136, China)

  • Chuyao Weng

    (School of Land Science and Technology, China University of Geosciences, Beijing 100083, China)

  • Wenxuan Wang

    (Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
    College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100101, China)

  • Yecui Hu

    (School of Land Science and Technology, China University of Geosciences, Beijing 100083, China)

Abstract

Rapid economic growth has a significant impact on land use change, which would threaten the natural ecology. Zhangye city of the Heihe River Basin, China is an ecologically vulnerable region where land use changes significantly due to socioeconomic development and population increases. The study employed a computable general equilibrium of land use change (CGELUC) model to simulate land use change and then used a dynamic land system (DLS) model to spatialize land use change during 2015–2030 under three development scenarios in Zhangye city. The three development scenarios are the baseline scenario (BAU), the resource consumption scenario (RCS) and the green development scenario (GDS). We found that economic growth would lead to land demand increases in high value-added industries and decreases in low value-added industries. The cultivated land would decrease while the built-up area would increase. By 2030, the cultivated land will decrease by 8.16%, 10.89% and 4.16%, respectively, under BAU, RCS and GDS, while the built-up area will increase by 8.61%, 10.39% and 4.75%, respectively. The expansion of built-up area under RCS presents spatial characteristics of centralized distribution, while spatial characteristics of uniform discrete distributions are presented under GDS. The expansion of ecological land under GDS would be considerable, especially in the north of Sunan County and Gaotai County, and around the natural reserve of Ganzhou County. This paper provides a scientific reference for coordinating economic development and ecological protection in the rapidly developing urbanized areas in western China.

Suggested Citation

  • Yuping Bai & Zhe Zhao & Chuyao Weng & Wenxuan Wang & Yecui Hu, 2021. "Scenario-Based Analysis of Land Use Competition and Sustainable Land Development in Zhangye of the Heihe River Basin, China," IJERPH, MDPI, vol. 18(19), pages 1-20, October.
    • Handle: RePEc:gam:jijerp:v:18:y:2021:i:19:p:10501-:d:650895
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1660-4601/18/19/10501/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1660-4601/18/19/10501/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Xie, Hualin & Chen, Qianru & Lu, Fucai & Wu, Qing & Wang, Wei, 2018. "Spatial-temporal disparities, saving potential and influential factors of industrial land use efficiency: A case study in urban agglomeration in the middle reaches of the Yangtze River," Land Use Policy, Elsevier, vol. 75(C), pages 518-529.
    2. Zhang, Honghui & Zeng, Yongnian & Jin, Xiaobin & Shu, Bangrong & Zhou, Yinkang & Yang, Xuhong, 2016. "Simulating multi-objective land use optimization allocation using Multi-agent system—A case study in Changsha, China," Ecological Modelling, Elsevier, vol. 320(C), pages 334-347.
    3. Aritta Suwarno & Meine van Noordwijk & Hans-Peter Weikard & Desi Suyamto, 2018. "Indonesia’s forest conversion moratorium assessed with an agent-based model of Land-Use Change and Ecosystem Services (LUCES)," Mitigation and Adaptation Strategies for Global Change, Springer, vol. 23(2), pages 211-229, February.
    4. Guan, DongJie & Li, HaiFeng & Inohae, Takuro & Su, Weici & Nagaie, Tadashi & Hokao, Kazunori, 2011. "Modeling urban land use change by the integration of cellular automaton and Markov model," Ecological Modelling, Elsevier, vol. 222(20), pages 3761-3772.
    5. Feng Wu & Jinyan Zhan & Qian Zhang & Zhongxiao Sun & Zhan Wang, 2014. "Evaluating Impacts of Industrial Transformation on Water Consumption in the Heihe River Basin of Northwest China," Sustainability, MDPI, vol. 6(11), pages 1-14, November.
    6. Yue Dou & Guolin Yao & Anna Herzberger & Ramon Felipe Bicudo da Silva & Qian Song & Ciara Hovis & Mateus Batistella & Emilio Moran & Wenbin Wu & Jianguo Liu, 2020. "Land-Use Changes in Distant Places: Implementation of a Telecoupled Agent-Based Model," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 23(1), pages 1-11.
    7. David Laborde & Hugo Valin, 2012. "MODELING LAND-USE CHANGES IN A GLOBAL CGE: ASSESSING THE EU BIOFUEL MANDATES WITH THE MIRAGE-BioF MODEL," Climate Change Economics (CCE), World Scientific Publishing Co. Pte. Ltd., vol. 3(03), pages 1-39.
    8. Bai, Yuping & Deng, Xiangzheng & Cheng, Yunfei & Hu, Yecui & Zhang, Lijin, 2021. "Exploring regional land use dynamics under shared socioeconomic pathways: A case study in Inner Mongolia, China," Technological Forecasting and Social Change, Elsevier, vol. 166(C).
    9. Liu, Xiaoping & Ou, Jinpei & Li, Xia & Ai, Bin, 2013. "Combining system dynamics and hybrid particle swarm optimization for land use allocation," Ecological Modelling, Elsevier, vol. 257(C), pages 11-24.
    10. Weng, Yuwei & Chang, Shiyan & Cai, Wenjia & Wang, Can, 2019. "Exploring the impacts of biofuel expansion on land use change and food security based on a land explicit CGE model: A case study of China," Applied Energy, Elsevier, vol. 236(C), pages 514-525.
    11. Gao, Xin & Zhang, Anlu & Sun, Zhanli, 2020. "How regional economic integration influence on urban land use efficiency？ A case study of Wuhan metropolitan area, China," Land Use Policy, Elsevier, vol. 90(C).
    12. Jin, Gui & Chen, Kun & Wang, Pei & Guo, Baishu & Dong, Yin & Yang, Jun, 2019. "Trade-offs in land-use competition and sustainable land development in the North China Plain," Technological Forecasting and Social Change, Elsevier, vol. 141(C), pages 36-46.
    13. Yu Liu & Xiaohong Hu & Qian Zhang & Mingbo Zheng, 2017. "Improving Agricultural Water Use Efficiency: A Quantitative Study of Zhangye City Using the Static CGE Model with a CES Water−Land Resources Account," Sustainability, MDPI, vol. 9(2), pages 1-15, February.
    14. Rasmussen, Laura Vang & Rasmussen, Kjeld & Reenberg, Anette & Proud, Simon, 2012. "A system dynamics approach to land use changes in agro-pastoral systems on the desert margins of Sahel," Agricultural Systems, Elsevier, vol. 107(C), pages 56-64.
    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. Guifang Li & Dongdong Ma & Cuiping Zhao & Hang Li, 2023. "The Effect of the Comprehensive Reform of Agricultural Water Prices on Farmers’ Planting Structure in the Oasis–Desert Transition Zone—A Case Study of the Heihe River Basin," IJERPH, MDPI, vol. 20(6), pages 1-17, March.

    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. Yu, Ziyue & Deng, Xiangzheng & Cheshmehzangi, Ali & Mangi, Eugenio, 2023. "Structural succession of land resources under the influence of different policies: A case study for Shanxi Province, China," Land Use Policy, Elsevier, vol. 132(C).
    2. Guadalupe Azuara García & Efrén Palacios Rosas & Alfonso García-Ferrer & Pilar Montesinos Barrios, 2017. "Multi-Objective Spatial Optimization: Sustainable Land Use Allocation at Sub-Regional Scale," Sustainability, MDPI, vol. 9(6), pages 1-21, June.
    3. Britz, Wolfgang & Li, Jingwen & Shang, Linmei, 2021. "Combining large-scale sensitivity analysis in Computable General Equilibrium models with Machine Learning: An Example Application to policy supporting the bio-economy," Conference papers 333285, Purdue University, Center for Global Trade Analysis, Global Trade Analysis Project.
    4. Xuesong Feng & Zhibin Tao & Xuejun Niu & Zejing Ruan, 2021. "Multi-Objective Land Use Allocation Optimization in View of Overlapped Influences of Rail Transit Stations," Sustainability, MDPI, vol. 13(23), pages 1-14, November.
    5. Yin Ma & Minrui Zheng & Xinqi Zheng & Yi Huang & Feng Xu & Xiaoli Wang & Jiantao Liu & Yongqiang Lv & Wenchao Liu, 2023. "Land Use Efficiency Assessment under Sustainable Development Goals: A Systematic Review," Land, MDPI, vol. 12(4), pages 1-21, April.
    6. Zhang, Han & Zheng, Jinhui & Hunjra, Ahmed Imran & Zhao, Shikuan & Bouri, Elie, 2024. "How does urban land use efficiency improve resource and environment carrying capacity?," Socio-Economic Planning Sciences, Elsevier, vol. 91(C).
    7. Qing Zhou & Yali Zhang & Feng Wu, 2022. "Can Water Price Improve Water Productivity? A Water-Economic-Model-Based Study in Heihe River Basin, China," Sustainability, MDPI, vol. 14(10), pages 1-18, May.
    8. Jing Yao & Xiaoxiang Zhang & Alan T. Murray, 2018. "Spatial Optimization for Land-use Allocation," International Regional Science Review, , vol. 41(6), pages 579-600, November.
    9. Chengzhen Song & Qingfang Liu & Jinping Song & Zhengyun Jiang & Zhilin Lu & Yueying Chen, 2022. "Land Use Efficiency in the Yellow River Basin in the Background of China’s Economic Transformation: Spatial-Temporal Characteristics and Influencing Factors," Land, MDPI, vol. 11(12), pages 1-22, December.
    10. Dang, Anh Nguyet & Kawasaki, Akiyuki, 2017. "Integrating biophysical and socio-economic factors for land-use and land-cover change projection in agricultural economic regions," Ecological Modelling, Elsevier, vol. 344(C), pages 29-37.
    11. Selamawit Haftu Gebresellase & Zhiyong Wu & Huating Xu & Wada Idris Muhammad, 2023. "Scenario-Based LULC Dynamics Projection Using the CA–Markov Model on Upper Awash Basin (UAB), Ethiopia," Sustainability, MDPI, vol. 15(2), pages 1-27, January.
    12. Weng, Yuwei & Cai, Wenjia & Wang, Can, 2021. "Evaluating the use of BECCS and afforestation under China’s carbon-neutral target for 2060," Applied Energy, Elsevier, vol. 299(C).
    13. Xue, Dan & Yue, Li & Ahmad, Fayyaz & Draz, Muhammad Umar & Chandio, Abbas Ali & Ahmad, Munir & Amin, Waqas, 2022. "Empirical investigation of urban land use efficiency and influencing factors of the Yellow River basin Chinese cities," Land Use Policy, Elsevier, vol. 117(C).
    14. Song, Yang & Yeung, Godfrey & Zhu, Daolin & Zhang, Lixin & Xu, Yang & Zhang, Lanyue, 2020. "Efficiency of logistics land use: The case of Yangtze River Economic Belt in China, 2000–2017," Journal of Transport Geography, Elsevier, vol. 88(C).
    15. Xiaojun Ye & Lingyun Fan & Cheng Lei, 2023. "Intensive-Use-Oriented Performance Evaluation and Optimization of Rural Industrial Land: A Case Study of Wujiang District, China," Sustainability, MDPI, vol. 15(11), pages 1-19, May.
    16. Yabo Zhao & Dixiang Xie & Xiwen Zhang & Shifa Ma, 2021. "Integrating Spatial Markov Chains and Geographically Weighted Regression-Based Cellular Automata to Simulate Urban Agglomeration Growth: A Case Study of the Guangdong–Hong Kong–Macao Greater Bay Area," Land, MDPI, vol. 10(6), pages 1-19, June.
    17. Dinghua Ou & Xingzhu Yao & Jianguo Xia & Xuesong Gao & Changquan Wang & Wanlu Chen & Qiquan Li & Zongda Hu & Juan Yang, 2019. "Development of a Composite Model for Simulating Landscape Pattern Optimization Allocation: A Case Study in the Longquanyi District of Chengdu City, Sichuan Province, China," Sustainability, MDPI, vol. 11(9), pages 1-35, May.
    18. Yuee Cao & Yunlu Jiang & Lin Feng & Ge Shi & Haotian He & Jianjun Yang, 2022. "Identification of Territorial Spatial Pattern Conflicts in Aksu River Basin, China, from 1990 to 2020," Sustainability, MDPI, vol. 14(22), pages 1-18, November.
    19. Yue Zhou & Yi Chen & Yi Hu, 2021. "Assessing Efficiency of Urban Land Utilisation under Environmental Constraints in Yangtze River Delta, China," IJERPH, MDPI, vol. 18(23), pages 1-18, November.
    20. Wenwen Tang & Lihan Cui & Sheng Zheng & Wei Hu, 2022. "Multi-Scenario Simulation of Land Use Carbon Emissions from Energy Consumption in Shenzhen, China," Land, MDPI, vol. 11(10), pages 1-16, September.

    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:jijerp:v:18:y:2021:i:19:p:10501-:d:650895. 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.