2022
DOI: 10.31497/zrzyxb.20220613
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Impact of urbanization on supply and demand of typical ecosystem services in Yangtze River Delta

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Cited by 14 publications
(16 citation statements)
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“…For the key urbanization region, the demand capacity of ESs was strong, but the supply capacity of ESs was weak. Therefore, the deficit of ESs in the key urbanization region needs to be balanced in regard to the whole HREEB region or a larger region [ 39 ]. The boundary restrictions of administrative regions need to be broken, and collaborative governance of ecosystems among regions needs to be achieved [ 33 ].…”
Section: Discussionmentioning
confidence: 99%
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“…For the key urbanization region, the demand capacity of ESs was strong, but the supply capacity of ESs was weak. Therefore, the deficit of ESs in the key urbanization region needs to be balanced in regard to the whole HREEB region or a larger region [ 39 ]. The boundary restrictions of administrative regions need to be broken, and collaborative governance of ecosystems among regions needs to be achieved [ 33 ].…”
Section: Discussionmentioning
confidence: 99%
“…Therefore, this study will use the ESs matrix method to measure the ESs supply, demand, and balance. Although existing literature has emphasized assessments of ES S&D, these studies did not sufficiently explore the spatial dependence of ES balance and its response to urbanization [ 28 , 37 , 38 , 39 ]. In addition, previous studies have been conducted on macro scales, such as the national scale, provincial scale, and prefecture-level city scale [ 21 , 30 , 35 , 40 ]; there is a lack of studies on the S&D of ESs on the multiple scales.…”
Section: Introductionmentioning
confidence: 99%
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“…Population density data was derived from WorldPOP ( , accessed on 27 November 2021); GDP density data was derived from the inversion of the regression relationship between GDP and land use and night lighting data for each city in YRD. Related calculation process can be found in Methods S1 [ 31 ]; the proportion of urban built-up area was counted using the regional statistics function in ArcGIS10.8. Land use data were obtained from the Resource and Environment Science Data Centre of the Chinese Academy of Sciences ( , accessed on 20 November 2021); urban land use data were obtained based on the Globeland30 land use dataset extracted from man-made surface types.…”
Section: Methodsmentioning
confidence: 99%
“…Carbon emissions from secondary and tertiary industries were inferred by constructing a regression model between nighttime lighting and carbon emissions within urban, rural, industrial, mining and residential land use types. The total carbon emissions of each industry were determined according to the proportion of total energy consumption of primary, secondary and tertiary industries in YRD [ 31 ].…”
Section: Methodsmentioning
confidence: 99%