Ecosystem Services Assessment, Trade-Off, and Bundles in the Yellow River Basin, China

Yang, Jie; Xie, Bin; Tao, Wenqian; Zhang, Degang

doi:10.3390/d13070308

Cited by 23 publications

(11 citation statements)

References 43 publications

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“…The accuracy of CS is primarily related to NPP [ 77 ], and the accuracy of CS is determined by comparing NPP values. The CS evaluation result in this paper agrees with Yang’s findings [ 78 ]. Song et al calculated HQs of 0.642 and 0.637 in 2000 and 2018 [ 79 ], respectively, which are comparable to 0.639 in 2000 and 0.637 in 2020 in this paper.…”

Section: Discussionsupporting

confidence: 91%

Trade-Off Analyses of Multiple Ecosystem Services and Their Drivers in the Shandong Yellow River Basin

Zhang

Zhao

et al. 2022

IJERPH

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With the intensification of conflicts between different ecosystem services, how to achieve a win-win situation between socio-economic development and ecological protection is an important issue that needs to be addressed nowadays. In particular, how to better quantify and assess the intensity of ecosystem service trade-offs and their relative benefits, and to identify the influencing factors are issues that need to be studied in depth. Based on the INVEST model, this paper analyzed the evolution of spatial and temporal patterns of ecosystem services such as Carbon Storage (CS), Food Production (FP), Habitat Quality (HQ), and Water Yield (WY) in the Shandong Yellow River Basin (SYRB) in 2000, 2010 and 2020. Next, we quantitatively measured the trade-off intensity and revealed the key influencing factors of the trade-off intensity evolution using automatic linear models, root mean square deviation, and geographically weighted regressions. Subsequently, we further analyzed the impact of the correlation between environmental and socio-economic factors on the trade-off intensity of ecosystem services. The results indicated that the temporal and spatial changes of the four main ecosystem services in SYRB area were inconsistent. WY showed a fluctuating trend, with a large interannual gap. CS and FP are on the rise, while HQ is on the decline. Spatially, WY and HQ showed a decreasing distribution from the center to the periphery, while FP and CS showed a decreasing distribution from the southwest to the northeast. The location characteristics of SYRB’s four ecosystem services and their trade-offs were obvious. FP had absolute location advantage in ecosystem service trade-offs. Most of the four ecosystem services showed significant trade-offs, and the trade-off intensity had significant spatial heterogeneity, but the trade-off between FP and CS was relatively weak. At the same time, there were also differences in the trends of trade-off intensities. Counties with low trade-off intensity were mostly located in mountainous areas; these areas are less disturbed by human activities, and most of them are areas without prominent services. Counties with high trade-off intensities were mostly concentrated in areas with relatively developed agriculture; these areas are more disturbed by human activities and are mostly prominent in FP. The trade-off intensity of ecosystem services in SYRB was affected by several factors together, and there were difference characteristics in the degree and direction of influence of each factor. Moreover, these influencing factors have gradually changed over 20 years. In terms of the spatial distribution at the county scale, the influence areas of the dominant drivers of different trade-off types varied greatly, among which the areas with NDVI, CON, and PRE as the dominant factors were the largest. In the future, in effectively balancing regional economic development and ecological environmental protection, quantifiable correspondence strategies should be developed from the administrative perspective of counties and regions based on comprehensive consideration of the locational advantages of each ecosystem service and changes in trade-offs.

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Section: Discussionsupporting

confidence: 91%

Trade-Off Analyses of Multiple Ecosystem Services and Their Drivers in the Shandong Yellow River Basin

Zhang

Zhao

et al. 2022

IJERPH

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“…The headwaters of the western Yellow River Basin have an average altitude of more than 4000 m, profuse with glacial landforms; the central area is between 1000–2000 m above sea level, with extensive loess landforms and serious soil erosion; and the eastern area has many alluvial plains and the river courses are higher than the ground, which is susceptible to floods. In general, the ecological environment of the Yellow River Basin is extremely fragile [ 27 , 53 ]. Secondly, the Yellow River Basin has abundant minerals and energy resources and is known as the “energy basin”.…”

Section: Discussionmentioning

confidence: 99%

Spatiotemporal evolution and influencing factors of urban shrinkage in the Yellow River Basin, China

Jin

Wang

et al. 2022

PLoS ONE

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The phenomenon of urban shrinkage has spread across the world as the political and economic landscape changes worldwide. The urban development in China has entered a post-development era characterized by coexisting urban expansion and local shrinkage whilst urbanization continues to progress in this country. This paper investigates the urban shrinkage of 80 cities in the Yellow River, China from the perspective of population and economy, based on urban shrinkage models drawing from western countries. It also analyzes the spatiotemporal evolution and influencing factors of urban shrinkage in this area using a spatial panel data model. The results show the following. (1) The phenomenon of urban shrinkage in the Yellow River Basin has gradually occurred and the degree of shrinkage has aggravated. (2) The shrinking cities in the Yellow River Basin are mainly population-related and potential. (3) The phenomenon of urban shrinkage in the Yellow River Basin shows significant spatiotemporal differences. Shrinking cities are mainly distributed in the upper and middle reaches of the Yellow River Basin and the number of shrinking cities has gradually increased over time. (4) In terms of influencing factors, financial, traffic, and medical conditions have a greater impact on population-related and economy-related shrinkage in these cities.

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“…Identifying the interrelationships between ecological functions is essential for understanding services and guiding decision-making However, the interactions between ecological functions are not all linear. Therefore, the interaction between them can be understood only by evaluating multiple functions [38]. This study selected five typical grassland ecological services, analyzed their trade-off sand synergistic relationships by adopting the correlation coefficient method and the bivariate spatial autocorrelation method, explored the temporal change characteristics of the trade-offs and synergistic relationships, and expressed the heterogeneity in space.…”

Section: Discussionmentioning

confidence: 99%

The Trade-Offs and Synergistic Relationships between Grassland Ecosystem Functions in the Yellow River Basin

2021

Self Cite

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Grassland is the most important land use/cover type in the Yellow River basin. Studying its ecosystem services and the trade-off and synergistic relationships between its various functions is of great significance to high-quality development and the protection of the ecological environment in the Yellow River basin. This paper evaluates the five typical functions of grassland in the Yellow River basin quantitatively, including water yield, carbon storage, soil conservation, habitat quality, and NPP by adopting the InVEST model and the CASA model. It analyzes changes in the trade-offs and the synergistic relationships between the five ecosystem functions from 1990 to 2018 by adopting the correlation coefficient method. The paper also analyzes and explores the spatial heterogeneity of the trade-offs and synergistic relationships by adopting the bivariate spatial autocorrelation method. The results show that from 1990 to 2018, the average water yield depth, carbon storage, and NPP of the grassland in the Yellow River basin tended to increase; soil conservation and habitat quality showed a decreasing trend; and the spatial distribution of the five functions were clearly in line with zonal law. The five ecological functions were synergistic; the synergistic relationship between water yield and the other functions was relatively weak, and there was a strong synergistic relationship between the other four functions. The trade-offs and synergistic relationships between the five functions demonstrated significant spatial heterogeneity in space. This research provides a scientific basis for determining the optimal utilization and sustainable development of grassland resources.

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Ecosystem Services Assessment, Trade-Off, and Bundles in the Yellow River Basin, China

Cited by 23 publications

References 43 publications

Trade-Off Analyses of Multiple Ecosystem Services and Their Drivers in the Shandong Yellow River Basin

Trade-Off Analyses of Multiple Ecosystem Services and Their Drivers in the Shandong Yellow River Basin

Spatiotemporal evolution and influencing factors of urban shrinkage in the Yellow River Basin, China

The Trade-Offs and Synergistic Relationships between Grassland Ecosystem Functions in the Yellow River Basin

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