Simulating the Spatial Mismatch between Ecosystem Services’ (ESs’) Supply and Demand Based on Their Spatial Transfer in Urban Agglomeration Area, China

Liu, Min; Fan, Jianpeng; Li, Yuanzheng; Sun, L.

doi:10.3390/land11081192

Cited by 5 publications

(5 citation statements)

References 39 publications

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“…In urban agglomerations, soil, hydrology, vegetation, water quality, and other parameters are greatly affected by human activities [40,44] and differ significantly from the natural state. There are currently few studies on ecosystem service evaluation in this region, and the basic data is lacking [23,30,39,45]. Therefore, it is necessary to obtain relevant basic data through field observation and investigation combined with the actual situation of the study area, input this basic data into the model, and optimise the model parameters to more accurately simulate the supply potential of ecosystem services in urban agglomeration areas.…”

Section: Discussionmentioning

confidence: 99%

Ecosystem Service Optimisation in the Central Plains Urban Agglomeration Based on Land Use Structure Adjustment

Liu¹,

Fan²,

Li³

et al. 2023

Preprint

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Based on multi-source remote sensing data, scenario analysis, and model simulation, the Pareto optimal solutions for water supply, water purification (N retention), and carbon storage and sequestration services under different scenarios were sought by adjusting its land use structure. The results showed that. In Scenario 1(S1), the water supply service needs to increase by 86.820 to 11.211 billion cubic metres, the water purification (N retention) service needs to decrease by 11,400 to 11,700 tons, and the carbon storage and sequestration service need to decrease by 2.070 to 2.487 billion tons. In Scenario 2(S2), the water supply service needs to increase by 8.243–10.666 billion cubic metres, the water purification (N retention) service needs to decrease by 11,300–1.10 million tons, and the carbon storage and sequestration service needs to decrease by 2.033 to 2.466 billion tons. In Scenario 3 (S3), the water supply service needs to increase by 7.832–11.437 billion cubic metres, the water purification (N retention) service needs to decrease by 1.16–10,800 tons, and the carbon storage and sequestration service needs to decrease by 19.220 to 2.380 billion tons. After land use optimisation and adjustment, the S3 ecological land structure is complete and consistent with the vision of ecological protection and urban development in the study area, which is the optimal scenario. After optimising the S3 ecosystem service supply pattern, the water supply, water purification (N retention), and carbon storage and sequestration services could connect the western and eastern ecosystem service supply areas, balance the overall ecosystem service supply pattern of the study area and meet the demand for ecosystem services. The results can guide regional land planning and ecosystem service management optimisation.

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Section: Discussionmentioning

confidence: 99%

Ecosystem Service Optimisation in the Central Plains Urban Agglomeration Based on Land Use Structure Adjustment

Liu¹,

Fan²,

Li³

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…In urban agglomerations, soil, hydrology, vegetation, water quality, and other parameters are greatly affected by human activities [49,53] and differ significantly from the natural state. There are currently few studies on ecosystem service evaluation in this region, and basic data are lacking [32,39,48,54,55]. Therefore, it is necessary to obtain relevant basic data through field observation and investigation combined with the actual situation of the study area, input these basic data into the model, and optimise the model parameters to more accurately simulate the supply potential of ecosystem services in urban agglomeration areas.…”

Section: Discussionmentioning

confidence: 99%

Ecosystem Service Optimisation in the Central Plains Urban Agglomeration Based on Land Use Structure Adjustment

Liu

Fan

et al. 2023

Land

Self Cite

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By using the methods of scenario analysis, model simulation, and the multi-objective spatial optimisation algorithm Non-dominated Sorting Genetic Algorithm-II (NSGA-II), the Pareto optimal solutions for water supply, water purification (N retention), as well as carbon storage and sequestration service (carbon service) of the Central Plains Urban Agglomeration (CPUA) were sought by adjusting the land use structure. It showed that, to reach the Pareto optimal solution goal, (1) in Scenario 1 (S1), the water supply service needs to increase by 10.682 billion cubic metres, the water purification (N retention) service needs to decrease by 11,400 tons, and the carbon service need to decrease by 2.487 billion tons. In Scenario 2 (S2), the water supply service needs to increase by 8.243 billion cubic metres, the water purification (N retention) service needs to decrease by 11,000 tons, and the carbon service needs to decrease by 2.466 billion tons. In Scenario 3 (S3), the water supply service needs to increase by 4.089 billion cubic metres, the water purification (N retention) service needs to decrease by 10,800 tons, and the carbon service needs to decrease by 2.380 billion tons. (2) After land use optimisation and adjustment, the S3 ecological land structure is complete and consistent with the vision of ecological protection and urban development in the study area, which is the optimal scenario. (3) Optimising the ecosystem service supply pattern through land use structure adjustment could balance the overall ecosystem service supply pattern of the study area In regions wherein ecosystem supply is insufficient and there is a spatial mismatch between supply and demand for ecosystem services, this study can guide regional land planning and assist in the formulation of ecosystem service management policies.

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“…The spatial transfer of ecosystem service values is a complex process due to differences in ecosystems and is influenced by multiple factors such as meteorology and hydrology. However, there is a basic consensus that ecosystem products could naturally move across regions [46]. The spatial transfer enables the GEP to transfer out of the habitat and function on a larger scale.…”

Section: Spatial Transfer Intensity Of the Gepmentioning

confidence: 99%

Spatial–Temporal Variations of the Gross Ecosystem Product under the Influence of the Spatial Spillover Effect of Urbanization and Ecological Construction in the Yangtze River Delta Region of China

Ji,

Qi,

Jiang

et al. 2024

Land

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Rapid development of urbanization and intense human activities had a profound influence on the ecosystem service functions. As an integrated monetary index for the evaluation of final ecosystem services, the gross ecosystem product (GEP) is widely used in the quantification of ecosystem service value (ESV). This study initially assessed and analyzed the spatial distribution of the GEP at the county-level scale using multisource data spanning 2000, 2005, 2010, 2015, and 2020. Then, the spatial transfer characteristics of the GEP were measured. Finally, the study employed spatial panel econometric models and the geographically weighted regression (GWR) model to investigate the spatial effect of urbanization and ecological construction on the GEP. The results indicated that: (1) In 2020, the GEP in the Yangtze River Delta Region was RMB 15.24 trillion, and the GEP per unit area was RMB 42.58 million per square kilometer. It exhibited a cumulative decrease of RMB 298.72 billion from 2000 to 2020. (2) The spatial transfer efficiency of the GEP in urban agglomerations showed a clear decline trend. During the period of 2000–2020, over 96% of county-level units exhibited a decline with RMB 90,076,103.17/km2, indicating a consistent downward trend from the central regions towards the periphery. (3) Based on the decomposition effects of the spatial Durbin mode, urbanization and the ecological construction indicator showed spatial spillover effects on the GEP, but their impact mechanisms varied substantially. Among them, the urbanization rate (UR), population density (PD), and the proportion of impervious land (ILP) had the largest negative effect on the GEP, and a 1% rise in ILP locally resulted in a 0.044% decline in the local GEP and a 0.078% rise in the GEP of neighboring units. And the area of ecological land had a positive effect on the GEP of both local and neighboring areas. Those conclusions can offer evidence in favor of encouraging ecologically responsible building practices and sustainable growth in urban agglomerations.

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Simulating the Spatial Mismatch between Ecosystem Services’ (ESs’) Supply and Demand Based on Their Spatial Transfer in Urban Agglomeration Area, China

Cited by 5 publications

References 39 publications

Ecosystem Service Optimisation in the Central Plains Urban Agglomeration Based on Land Use Structure Adjustment

Ecosystem Service Optimisation in the Central Plains Urban Agglomeration Based on Land Use Structure Adjustment

Ecosystem Service Optimisation in the Central Plains Urban Agglomeration Based on Land Use Structure Adjustment

Spatial–Temporal Variations of the Gross Ecosystem Product under the Influence of the Spatial Spillover Effect of Urbanization and Ecological Construction in the Yangtze River Delta Region of China

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