IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v15y2023i12p9689-d1172978.html
   My bibliography  Save this article

Research on the Trade-Offs and Synergies of Ecosystem Services and Their Impact Factors in the Taohe River Basin

Author

Listed:
  • Jing Zhou

    (College of Geography and Environmental Science, Northwest Normal University, Lanzhou 730070, China)

  • Bo Zhang

    (College of Geography and Environmental Science, Northwest Normal University, Lanzhou 730070, China)

  • Yaowen Zhang

    (College of Tourism, Lanzhou University of Arts and Science, Lanzhou 730000, China)

  • Yuhan Su

    (College of Foreign Languages, Hebei Normal University, Shijiazhuang 050000, China)

  • Jie Chen

    (College of Geography and Environmental Science, Northwest Normal University, Lanzhou 730070, China)

  • Xiaofang Zhang

    (College of Geography and Environmental Science, Northwest Normal University, Lanzhou 730070, China)

Abstract

The Taohe River Basin is an essential ecological function area in the upper reaches of the Yellow River. Understanding the intricate trade-offs and synergies between ecosystem services (ESs) and exploring the impact of different factors are essential for achieving win–win outcomes in ecosystem management and socioeconomic development. The role of impact factors on the relationship between ESs, nevertheless, is more challenging to spatialize. This study used different models to estimate the net primary productivity (NPP), water yield (WY), and soil conservation (SC), and analyzed synergies and trade-offs between Ess. The spatial heterogeneity of the effects of natural and social factors on the relationships between Ess was explored using a geographic detector and a multi-scale geographically weighted regression (MGWR) model. The results show that: (1) NPP, WY, and SC all exhibit a rising trend, with multi-year averages of 488.99 gC/m 2 , 157.29 mm, and 1441.51 t/hm 2 , respectively; (2) NPP–WY and NPP–SC exhibit trade-offs in the majority of regions, while WY–SC are primarily synergistic in the upper and middle reaches, and they have the highest percentage of cropland, forest, and grassland; and (3) precipitation (PRE) has the greatest impact on the trade-off between NPP–WY and NPP–SC in the upper and middle reaches, and the gross domestic product (GDP), population density (POP), and distance from cropland (CROP) are the primary factors determining the synergy between NPP and WY in the lower reaches of the Loess Plateau cropping sector. PRE, digital elevation model (DEM), and CROP are the primary impact factors affecting the synergy of WY–SC. This study may serve as a reference for examining the evolutionary mechanism underlying the trade-offs and synergies between ESs and provide a scientific basis for future ecological environmental protection and regional land management in the Taohe River Basin.

Suggested Citation

  • Jing Zhou & Bo Zhang & Yaowen Zhang & Yuhan Su & Jie Chen & Xiaofang Zhang, 2023. "Research on the Trade-Offs and Synergies of Ecosystem Services and Their Impact Factors in the Taohe River Basin," Sustainability, MDPI, vol. 15(12), pages 1-18, June.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:12:p:9689-:d:1172978
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/15/12/9689/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/15/12/9689/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. M. Fathurahman & Purhadi & Sutikno & Vita Ratnasari, 2020. "Geographically Weighted Multivariate Logistic Regression Model and Its Application," Abstract and Applied Analysis, Hindawi, vol. 2020, pages 1-10, August.
    2. A. Stewart Fotheringham & Wenbai Yang & Wei Kang, 2017. "Multiscale Geographically Weighted Regression (MGWR)," Annals of the American Association of Geographers, Taylor & Francis Journals, vol. 107(6), pages 1247-1265, November.
    3. Feng, Zhe & Jin, Xueru & Chen, Tianqian & Wu, Jiansheng, 2021. "Understanding trade-offs and synergies of ecosystem services to support the decision-making in the Beijing–Tianjin–Hebei region," Land Use Policy, Elsevier, vol. 106(C).
    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. Zhenjun Yan & Yirong Wang & Xu Hu & Wen Luo, 2023. "Assessment and Enhancement of Ecosystem Service Supply Efficiency Based on Production Possibility Frontier: A Case Study of the Loess Plateau in Northern Shaanxi," Sustainability, MDPI, vol. 15(19), pages 1-20, September.
    2. Meirong Deng & Dehua Mao & Yeye Li & Ting Wang & Zui Hu, 2023. "Spatiotemporal Variation in Water-Related Ecosystem Services during 2000–2020 and Ecological Management Zoning in the Xiangjiang River Basin, China," Sustainability, MDPI, vol. 15(22), pages 1-23, November.

    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. Yanzhao Wang & Jianfei Cao, 2023. "Examining the Effects of Socioeconomic Development on Fine Particulate Matter (PM2.5) in China’s Cities Based on Spatial Autocorrelation Analysis and MGWR Model," IJERPH, MDPI, vol. 20(4), pages 1-23, February.
    2. Wang, Xiaoxi & Zhang, Yaojun & Yu, Danlin & Qi, Jinghan & Li, Shujing, 2022. "Investigating the spatiotemporal pattern of urban vibrancy and its determinants: Spatial big data analyses in Beijing, China," Land Use Policy, Elsevier, vol. 119(C).
    3. Hengyu Gu & Hanchen Yu & Mehak Sachdeva & Ye Liu, 2021. "Analyzing the distribution of researchers in China: An approach using multiscale geographically weighted regression," Growth and Change, Wiley Blackwell, vol. 52(1), pages 443-459, March.
    4. Shichao Lu & Zhihua Zhang & M. James C. Crabbe & Prin Suntichaikul, 2024. "Effects of Urban Land-Use Planning on Housing Prices in Chiang Mai, Thailand," Land, MDPI, vol. 13(8), pages 1-13, July.
    5. Jin, Peizhen & Mangla, Sachin Kumar & Song, Malin, 2021. "Moving towards a sustainable and innovative city: Internal urban traffic accessibility and high-level innovation based on platform monitoring data," International Journal of Production Economics, Elsevier, vol. 235(C).
    6. Chunfang Zhao & Yingliang Wu & Yunfeng Chen & Guohua Chen, 2023. "Multiscale Effects of Hedonic Attributes on Airbnb Listing Prices Based on MGWR: A Case Study of Beijing, China," Sustainability, MDPI, vol. 15(2), pages 1-21, January.
    7. Jing Duan & Pu Shi & Yuanyuan Yang & Dongyan Wang, 2024. "Spatiotemporal Change Analysis and Multi-Scenario Modeling of Ecosystem Service Values: A Case Study of the Beijing-Tianjin-Hebei Urban Agglomeration, China," Land, MDPI, vol. 13(11), pages 1-21, October.
    8. Li Gao & Mingjing Huang & Wuping Zhang & Lei Qiao & Guofang Wang & Xumeng Zhang, 2021. "Comparative Study on Spatial Digital Mapping Methods of Soil Nutrients Based on Different Geospatial Technologies," Sustainability, MDPI, vol. 13(6), pages 1-19, March.
    9. Li, Mengya & Kwan, Mei-Po & Hu, Wenyan & Li, Rui & Wang, Jun, 2023. "Examining the effects of station-level factors on metro ridership using multiscale geographically weighted regression," Journal of Transport Geography, Elsevier, vol. 113(C).
    10. Yang Yi & Chen Zhang & Jinqi Zhu & Yugang Zhang & Hao Sun & Hongzhang Kang, 2022. "Spatio-Temporal Evolution, Prediction and Optimization of LUCC Based on CA-Markov and InVEST Models: A Case Study of Mentougou District, Beijing," IJERPH, MDPI, vol. 19(4), pages 1-23, February.
    11. Sarni Maniar Berliana & Purhadi & Sutikno & Santi Puteri Rahayu, 2020. "Parameter Estimation and Hypothesis Testing of Geographically Weighted Multivariate Generalized Poisson Regression," Mathematics, MDPI, vol. 8(9), pages 1-14, September.
    12. Moore, David & Webb, Amanda L., 2022. "Evaluating energy burden at the urban scale: A spatial regression approach in Cincinnati, Ohio," Energy Policy, Elsevier, vol. 160(C).
    13. Jack C. Yue & Ming-Huei Tu & Yin-Yee Leong, 2024. "A spatial analysis of the health and longevity of Taiwanese people," The Geneva Papers on Risk and Insurance - Issues and Practice, Palgrave Macmillan;The Geneva Association, vol. 49(2), pages 384-399, April.
    14. Hosseinzadeh, Aryan & Algomaiah, Majeed & Kluger, Robert & Li, Zhixia, 2021. "Spatial analysis of shared e-scooter trips," Journal of Transport Geography, Elsevier, vol. 92(C).
    15. Yigong Hu & Binbin Lu & Yong Ge & Guanpeng Dong, 2022. "Uncovering spatial heterogeneity in real estate prices via combined hierarchical linear model and geographically weighted regression," Environment and Planning B, , vol. 49(6), pages 1715-1740, July.
    16. Yongxin Liu & Yiting Wang & Yiwen Lin & Xiaoqing Ma & Shifa Guo & Qianru Ouyang & Caige Sun, 2023. "Habitat Quality Assessment and Driving Factors Analysis of Guangdong Province, China," Sustainability, MDPI, vol. 15(15), pages 1-23, July.
    17. Tao Wang & Kai Zhang & Keliang Liu & Keke Ding & Wenwen Qin, 2023. "Spatial Heterogeneity and Scale Effects of Transportation Carbon Emission-Influencing Factors—An Empirical Analysis Based on 286 Cities in China," IJERPH, MDPI, vol. 20(3), pages 1-17, January.
    18. Mengxue Liu & Xiaobin Dong & Xuechao Wang & Bingyu Zhao & Hejie Wei & Weiguo Fan & Chenyang Zhang, 2022. "The Trade-Offs/Synergies and Their Spatial-Temporal Characteristics between Ecosystem Services and Human Well-Being Linked to Land-Use Change in the Capital Region of China," Land, MDPI, vol. 11(5), pages 1-22, May.
    19. Rémy Le Boennec & Julie Bulteau & Thierry Feuillet, 2022. "The role of commuter rail accessibility in the formation of residential land values: exploring spatial heterogeneity in peri-urban and remote areas," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 69(1), pages 163-186, August.
    20. Lu, Haiyan & Zhao, Pengjun & Hu, Haoyu & Zeng, Liangen & Wu, Kai Sheng & Lv, Di, 2022. "Transport infrastructure and urban-rural income disparity: A municipal-level analysis in China," Journal of Transport Geography, Elsevier, vol. 99(C).

    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:jsusta:v:15:y:2023:i:12:p:9689-:d:1172978. 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.