Influence of the habitat change for birds on community structure

Wang, Yujun; Xu, Jinhua; Chen, Jing; BingLin, Wu; Qing-bin, LU

doi:10.1016/j.chnaes.2013.09.003

Cited by 31 publications

(8 citation statements)

References 20 publications

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“…This approach hence has a stronger potential and more certainty than general statistics. and is strongly suggestive of causality as well [44].…”

Section: Research On In Uencing Factors Of Carbon Emissionmentioning

confidence: 89%

Spatio temporal variation of urban multi-scale carbon emissions and its influencing factors based on night light data:-A case study of Pearl River Delta urban agglomeration

LIU¹,

Zhou²,

ZHANG³

2023

Preprint

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Background: It is of great significance to formulate differentiated carbon emission reduction policies to clarify spatio-temporal characteristics and driving factors of carbon emissions in different cities and cities at different scales. By fitting nighttime light data of long time series from 2000 to 2020, a carbon emission estimation model of Pearl River Delta urban agglomeration, at city, county, and grid unit levels was built to quickly and accurately estimate carbon emission in the Delta cities above county level. Combining spatial statistics, spatial autocorrelation, emerging spatio-temporal hotspot analysis, and Theil index, this study explored the spatio-temporal differentiation of urban carbon emissions in the Delta , and used a geographical detector, to dig influencing factors of the differentiation. Results: The results of the study showed that night light data could replace a statistical yearbook in calculating carbon emissions of cities at or above county level. The calculation error was less than 11% in Pearl River Delta urban agglomeration. The three levels of carbon emissions in the Delta increased in a fluctuating manner, and the spatial distribution difference of carbon emissions at municipal and county levels was small. Therefore, a combination of municipal and county scales can be implemented to achieve precise emission reduction at both macro and micro levels. The central and eastern parts of the agglomeration, including Guangzhou, Shenzhen, Zhongshan, and Huizhou, were a high-value clustering and spatio-temporal hot spots of carbon emissions. Zhaoqing City in the northwestern part of the agglomeration had always been a low-value clustering and spatio-temporal cold spot, because of its population, economy, and geographical location . The carbon emission differences of the Delta cities were mainly caused by carbon emission differences within the cities at municipal level, and the cities faced the challenge of regional differences in the reduction of per capita carbon emissions. As the most influential single factor, spatial interaction between economic development and various factors was the main driving force for the growth of carbon emissions. Conclusions: Our study provide scientific theory and information support for carbon emission estimation and prediction, differentiated emission reduction measures, and carbon neutrality of cities in Pearl River Delta.

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“…This approach hence has a stronger potential and more certainty than general statistics. and is strongly suggestive of causality as well [44].…”

Section: Research On In Uencing Factors Of Carbon Emissionmentioning

confidence: 89%

Spatio temporal variation of urban multi-scale carbon emissions and its influencing factors based on night light data:-A case study of Pearl River Delta urban agglomeration

LIU¹,

Zhou²,

ZHANG³

2023

Preprint

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“…Spatial heterogeneity testing is a necessary part of spatial data analysis ( 36 ), and should first detect the presence of spatial heterogeneity to determine whether to use a global or local model. Geographic probes were used to analyze the spatially stratified heterogeneity of health productivity of health resources.…”

Section: Methodsmentioning

confidence: 99%

The spatio-temporal pattern and its influencing factors of production efficiency of health resources in China

Tang,

Ao,

2024

Front. Public Health

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There is always a contradiction between the limited health resources and the unlimited demand of the population for health services, and only by improving the productivity of health resources can the health level of the population be improved as much as possible. Using prefecture-level administrative regions as spatial units, the paper analyzes the spatial pattern and changes of health productivity of health resources in China from 2000 to 2010, and uses a spatial panel Tobit model to examine the effects of factors such as technical level of health institutions, health service accessibility, public health policies and ecological environment quality on health productivity of health resources. The results show that with the Hu Huanyong line as the dividing line, the spatial heterogeneity of “high in the southeast and low in the northwest” in the health productivity of China's health resources is clear; as the regional differences narrow, the spatial correlation increases, and the spatial pattern of “overall dispersion and partial agglomeration” becomes more obvious. The fitting results of the spatial Durbin model reveal the direction and degree of influence of local and adjacent factors on the production efficiency of health resources. The positive influence of technical level of local health institutions and the accessibility of health services, the literacy level and the ability to pay for health services of residents in adjacent areas, the degree of urbanization of regional health resource allocation, climate suitability and the quality of the atmospheric environment are significant. And the negative influence of local residents' literacy and ability to pay for health services, the technical level of health institutions in adjacent areas and the degree of medicalization of health resource allocation are also significant. The influence of the degree of medicalization of local health resource allocation and the accessibility of health services in adjacent areas are significantly spatial-heterogeneous.

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“…We used the ‘factor detector’ module within Geodetector15 in order to analyse the extent to which certain factors (table 1) explained the spatial heterogeneity of IMR. We quantified the influence q of each factor on IMR using the equation 22…”

Section: Methodsmentioning

confidence: 99%

Province-level distribution and drivers of infant mortality in mainland China: a Geodetector-based analysis of data from 2020

Zhang,

Tang,

Zhang

et al. 2023

BMJ Open

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ObjectiveThe present study investigated the province-level distribution and drivers of infant mortality rate (IMR) in mainland China.DesignEcological analysis based on publicly available data for all 31 provinces in mainland China.Data sourcesData on province-level IMRs in 2020 were obtained from the official websites of the healthcare commissions within each province and from the China Health Statistics Yearbook 2021. Data on potential IMR drivers were retrieved from the China Statistical Yearbook 2021.Data analysisGeoDa V.1.12.1 and ArcMap V.10.2 software were used to examine province-level distribution of IMR. Global and local spatial autocorrelations were performed, and Getis-ord G* hotspots and coldspots were identified. Geodetector was used to analyse the individual and joint influence of drivers on IMR.ResultsIMRs in 2020 varied from 1.91 to 7.60 per 1000 live births across provinces. The following statistically significant drivers with q values >0.5 were identified: health literacy of the population (0.6673), male illiteracy rate (0.6433), proportion of the population older than >65 years (0.6369), per capita government health expenditure (0.6216), forest coverage rate (0.5820), per capita disposable income (0.5785), per capita number of hospitals (0.5592), per capita gross regional product (0.5410) and sulfur dioxide concentration in the atmosphere (0.5158). The following three interactions among these drivers emerged as strongest influences on province-level IMR: proportion of population >65 years ∩ per capita gross regional product (q=0.9653), forest coverage rate ∩ per capita gross regional product (0.9610) and per capita government health expenditure ∩ sulfur dioxide (0.9295).ConclusionIMR in mainland China varies substantially across the country, being generally high-west and low-east. Several factors, on their own and interacting together, contribute to IMR. Policies and programmes to reduce IMR should be formulated according to local conditions and should focus on western provinces of the country.

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Influence of the habitat change for birds on community structure

Cited by 31 publications

References 20 publications

Spatio temporal variation of urban multi-scale carbon emissions and its influencing factors based on night light data:-A case study of Pearl River Delta urban agglomeration

Spatio temporal variation of urban multi-scale carbon emissions and its influencing factors based on night light data:-A case study of Pearl River Delta urban agglomeration

The spatio-temporal pattern and its influencing factors of production efficiency of health resources in China

Province-level distribution and drivers of infant mortality in mainland China: a Geodetector-based analysis of data from 2020

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