Threshold effect of ecosystem services in response to climate change, human activity and landscape pattern in the upper and middle Yellow River of China

Guo, Shanshan; Wu, Changyue; Wang, Yinghong; Qiu, Guoqiang; Zhu, Di; Niu, Qinghe; Qin, Lin

doi:10.1016/j.ecolind.2022.108603

Cited by 33 publications

(11 citation statements)

References 55 publications

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“…where subscripts i and t represent city and year, respectively; W is the selected spatial weight matrix. α 1 , α 2 , α 3 and θ 1 , θ 2 , θ 3 are the estimation coefficients of static SDM 10 Science Progress 106 (1) and dynamic SDM, respectively; and β 1 , β 2 , β 3 and σ 1 , σ 2 , σ 3 are the spatial interaction term coefficient of the corresponding variable. M it is the explained variable, which represents the ecological TFP (MIL it ) and its decomposition terms (MILEC it , MILTC it ).…”

Section: Methodsmentioning

confidence: 99%

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Temporal and spatial evolution and influencing factors of urban ecological total factor productivity in the Yellow River basin under strong sustainable development

et al. 2023

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A growing consensus worldwide has indicated the need to protect the ecological environment and achieve sustainable development. Ensuring ecological protection and high-quality development of the Yellow River basin have become China's major national strategy. We reviewed extant literature, summarised government reports and guidance documents on the Yellow River basin, and proposed introducing a strong sustainable development theory into the study of total factor productivity (TFP). The spatial–temporal evolution and influencing factors of urban ecological TFP in the Yellow River basin are of great practical significance. We proposed a new ecological TFP indicator: the modified input-oriented Luenberger productivity indicator (MIL). Using panel data from 78 cities in the Yellow River basin during 2003–2019, we measured the urban ecological TFP. We adopted the geographic information system tool and kernel density estimation to analyse the temporal and spatial evolution of the indicator, as well as its spatial effects and influencing factors, using the global Moran's I index and dynamic spatial Durbin model (SDM). Our results show that, during the sample period, our indicator increased in cities in the region with an average annual growth rate of 0.627%, driven by technological progress. The average annual growth rate in urban areas showed a decreasing distribution of ‘downstream-midstream-upstream’. Fiscal decentralisation (FD), industrial structure (IND), financial development (FIN), urbanisation level (URB) and research and development (RD) investment improved growth rates in this and the adjacent regions through direct and indirect effects. However, environmental regulation (ER), opening level (OPEN) of cities and population density (POP) were obstacles to TFP growth.

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Section: Methodsmentioning

confidence: 99%

“…Notes: Calculation: Calculated by vertical and horizontal scatter grade method, CUSY: China Urban Statistical Yearbook, USY: Urban Statistical Yearbooks of cities in the Yellow River basin. For the data used in this study, the timeframe was from 2003 to 2019.14Science Progress 106(1) …”

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confidence: 99%

Temporal and spatial evolution and influencing factors of urban ecological total factor productivity in the Yellow River basin under strong sustainable development

et al. 2023

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“…Using FRAGSTATS software, the actual situation of Urumqi city was taken into account with the previous research results [31][32][33], and a moving window of 1200 m was used to study the fragmentation and diversity of the landscape patterns in Urumqi. Landscape indices condense information on landscape patterns and can be used to analyze different ecological processes at each scale to demonstrate the structural features and spatial and temporal pattern development of the eco-landscape [34,35]. On the basis of earlier studies on LER evaluation [36,37] and combined with the characteristics of the study area and the ecological relevance of the landscape index, the calculations of repeated values and smaller values were discarded after repeated experiments.…”

Section: Data Sources and Pre-processingmentioning

confidence: 99%

Construction and Restoration of Landscape Ecological Network in Urumqi City Based on Landscape Ecological Risk Assessment

et al. 2022

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The ecological protection and sustainable development of Urumqi have become an important part of the high-quality growth of the urban agglomeration on the northern slope of Tianshan Mountain. Under the impacts of multi-source factors, the ecological landscape pattern of Urumqi has changed due to it being in a fragile eco-environment, so an ecological network is desperately needed to enhance ecological security patterns. Taking Urumqi city as the study area, the ecological risk evaluation model and the minimum cumulative resistance model were integrated to analyze the spatial and temporal features of landscape ecological risk from 2000 to 2020, and the future land use simulation model was used to predict the ecological risk pattern of Urumqi in 2030, construct a landscape ecological network, and propose ecological security protection strategies. Since 2000, land use in Urumqi has undergone drastic changes: the built-up land area has increased significantly, the landscape has diversified, and landscape fragmentation has shown a decreasing trend from the main urban area as the core to the urban fringe. The high-risk landscape ecology shows a decreasing trend from east to west, mainly in the bare land areas with sparse vegetation, whereas the risk is relatively low in woodland, arable land, and built-up areas. The change of risk in the study area is mainly influenced by the typical defective factors of oasis cities such as urban expansion, land desertification, and sparse vegetation. The landscape ecological network is mainly located in the southwest, central, and east of the study area, whereas there is no corridor distribution in the north and southeast, which is mainly caused by the special geographical location and climatic conditions. The ecological network mainly consists of 10 ecological sources and 10 ecological corridors and proposes conservation strategies for the optimization of the landscape pattern and for the construction of the ecological security pattern in Urumqi, providing a guide for the improvement of ecological security.

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“…As a result of these characteristics, the Yihe River Basin serves as a pivotal study area, exemplifying the impact of landscape pattern changes on ESVs. Although the existing research methods on the response mechanism of ESV caused by changes in watershed landscape patterns are relatively mature, they are mostly concentrated in the Yangtze River, Yellow River Basin, and their tributaries [22,[45][46][47][48][49][50]. On the contrary, little attention has been paid to the empirical analysis of the relationship between landscape patterns and ESVs in the typical mountain-hill-plain areas with abundant natural resources in the Central Plains [51], which need further study.…”

Section: Introductionmentioning

confidence: 99%

Identifying the Relationships between Landscape Pattern and Ecosystem Service Value from a Spatiotemporal Variation Perspective in a Mountain–Hill–Plain Region

Han,

Li,

Wei

et al. 2023

Forests

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Identifying the changes in landscape pattern and ecosystem service value (ESV) and clarifying their relationship in temporal changes and spatial variations can provide insight into regional landscape features and scientific support for regional landscape planning. Leveraging land use data from the Yihe River Basin, we quantitatively assessed the landscape pattern and ESV shifts spanning from 2000 to 2018 using the landscape pattern indexes and the equivalence factor method. We employed Pearson correlation metrics and the geographically weighted regression model to explore the interrelation of their spatiotemporal variations. Our results show the following: (1) Forestland represents the most expansive land cover category. Apart from construction land, all other types experienced a decline in area. The most notable change occurred in the area of construction land. (2) The aggregation of the overall landscape shows a downward trend. The levels of fragmentation, landscape diversity, and richness increased. (3) Throughout the entire study period, the overall ESV gradually decreased, and the land cover type with the greatest contribution to the ESV was forestland. (4) In terms of temporal changes, the patch density and edge density of the overall area are significantly negatively correlated with total ESVs. The largest values for the patch index, perimeter–area fractal dimension (PAFRAC), and aggregation are significantly positively correlated with total ESVs. (5) In terms of spatial variation, the contagion index (CONTAG), PAFRAC, and the Shannon diversity index (SHDI) were noticeably correlated with ESVs. The CONTAG is positively correlated with ESVs upstream, but negatively midstream and downstream. The SHDI is negatively correlated with ESVs upstream, but positively midstream and downstream. The PAFRAC exhibits a positive correlation with ESVs for the most part. The association between the landscape pattern indexes and ESVs exhibits temporal and spatial inconsistencies in most instances, suggesting a spatiotemporal scale effect in their relationship. This study recommends that the local government devises a long-term strategy for urban development and exercises stringent control over the unregulated expansion of construction land. Through reasonable territorial spatial planning, government departments could enhance the connectivity of the overall landscape pattern of the Yihe River Basin to achieve the reasonable allocation and sustainable development of regional resources.

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Threshold effect of ecosystem services in response to climate change, human activity and landscape pattern in the upper and middle Yellow River of China

Cited by 33 publications

References 55 publications

Temporal and spatial evolution and influencing factors of urban ecological total factor productivity in the Yellow River basin under strong sustainable development

Temporal and spatial evolution and influencing factors of urban ecological total factor productivity in the Yellow River basin under strong sustainable development

Construction and Restoration of Landscape Ecological Network in Urumqi City Based on Landscape Ecological Risk Assessment

Identifying the Relationships between Landscape Pattern and Ecosystem Service Value from a Spatiotemporal Variation Perspective in a Mountain–Hill–Plain Region

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