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Sustainability
Article . 2023 . Peer-reviewed
License: CC BY
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Sustainability
Article . 2023
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Ecosystem Services Supply–Demand Matching and Its Driving Factors: A Case Study of the Shanxi Section of the Yellow River Basin, China

Authors: Mingjing Xu; Qiang Feng; Shurong Zhang; Meng Lv; Baoling Duan;

Ecosystem Services Supply–Demand Matching and Its Driving Factors: A Case Study of the Shanxi Section of the Yellow River Basin, China

Abstract

Understanding the supply–demand relationships and driving mechanisms of ecosystem services (ES) provides a theoretical foundation for sustainable ecosystem management. This study utilized Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) models and geographical detectors to quantify the spatial–temporal patterns of the supply, demand, and supply–demand ratio of ESs such as water yield, soil conservation, and carbon sequestration, along with their driving factors, in the Shanxi section of the Yellow River Basin. The results show that: (1) From the year 2000 to 2020, although the supply and demand of water yield, soil conservation, and carbon sequestration fluctuated, they generally increased during this period of time. In comparison to ecosystem services from the year 2000 to 2020, the supply of water yield exceeded the demand in 2020. The supply, demand, and supply–demand ratio of ESs exhibited notable spatial heterogeneity. (2) The most notable factors influencing the supply–demand ratio of water yield varied between 2000 and 2020. In 2000, construction land was the most important factor, while in 2020, cropland had the greatest impact. However, the primary factors affecting the supply–demand ratio of soil conservation and carbon sequestration remained the same in 2000 and 2020. Forestland was the primary factor in 2000, while construction land was the primary factor in 2020. (3) Considering interaction factors, the interaction factors between construction land and precipitation had the greatest impact on the supply–demand ratio of water yield in 2000, while the interaction between forestland and cropland had the greatest impact in 2020. The interaction between cropland and shrubland had the greatest impact on the supply–demand ratio of soil conservation in 2000, whereas the interaction factors between construction land and forestland had the greatest impact in 2020. The interaction between construction land and shrubland had the greatest impact on the supply–demand ratio of carbon sequestration in 2000, while the interaction between construction land and cropland had the greatest impact in 2020. Overall, the interaction between construction land and various land-use factors had the strongest explanation for the supply–demand ratio of ecosystem services. This study can serve as a reference for the comprehensive development and utilization of the Shanxi section of the Yellow River Basin.

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Keywords

Environmental effects of industries and plants, Shanxi section of the Yellow River Basin, TJ807-830, TD194-195, Renewable energy sources, Environmental sciences, supply and demand patterns, GE1-350, ecosystem services; supply and demand patterns; driving factors; Shanxi section of the Yellow River Basin, ecosystem services, driving factors

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    citations
    This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    9
    popularity
    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
    Top 10%
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    Average
    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
    Top 10%
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citations
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
9
Top 10%
Average
Top 10%
gold
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