Spatio-temporal trends and influencing factors of PM2.5 concentrations in urban agglomerations in China between 2000 and 2016

Huang, Caihong; Liu, Kai; Zhou, Liang

doi:10.1007/s11356-020-11357-z

Cited by 47 publications

(22 citation statements)

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“…The two variables are spatially connected in case the spatial distribution is consistent. Geodetector can reflect the interaction between different factors and variables and can be used to explore the factors influencing resources and the environment [ 53 , 54 ]. In this paper, Geodetector was used to simulate the factors influencing UCEs.…”

Section: Data Sources and Methodsmentioning

confidence: 99%

Spatial Differences and Influential Factors of Urban Carbon Emissions in China under the Target of Carbon Neutrality

Liu

Ren

et al. 2022

IJERPH

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Cities are areas featuring a concentrated population and economy and are major sources of carbon emissions (CEs). The spatial differences and influential factors of urban carbon emissions (UCEs) need to be examined to reduce CEs and achieve the target of carbon neutrality. This paper selected 264 cities at the prefecture level in China from 2008 to 2018 as research objects. Their UCEs were calculated by the CE coefficient, and the spatial differences in them were analyzed using exploratory spatial data analysis (ESDA). The influential factors of UCEs were studied with Geodetector. The results are as follows: (1) The UCEs were increasing gradually. Cities with the highest CEs over the study period were located in the urban agglomerations of Beijing–Tianjin–Hebei, Yangtze River Delta, Pearl River Delta, middle reaches of the Yangtze River, and Chengdu–Chongqing. (2) The UCEs exhibited certain global and local spatial autocorrelations. (3) The industrial structure was the dominant factor influencing UCEs.

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Section: Data Sources and Methodsmentioning

confidence: 99%

Spatial Differences and Influential Factors of Urban Carbon Emissions in China under the Target of Carbon Neutrality

Liu

Ren

et al. 2022

IJERPH

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“…The statistic q can be used to measure the spatial differentiation, explore explanatory factors, and analyze the interaction between variables. It has been widely used to examine factors influencing the atmospheric environment (Zhou et al, 2019;Huang et al, 2020). The factors influencing atmospheric environmental efficiency were measured using this method.…”

Section: Geodetectormentioning

confidence: 99%

Assessing urban atmospheric environmental efficiency and factors influencing it in China

Liu

Wang

Zhang

2021

Environ Sci Pollut Res

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The urban atmospheric environmental efficiency in China shows an upward trend.Urban atmospheric environmental efficiency is highest in Eastern China and lowest in Western China.Urban atmospheric environmental efficiency has a significant global spatial autocorrelation.High-high and low-low are main types of efficiency in local spatial autocorrelation.Population density, industrialization, and technology influence this efficiency.

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“…However, the urban agglomerations also produce over 75% of the total pollution outputs, which consequently overload the ecological environment of the urban agglomerations (Fang et al, 2017). Compared with city individual, material and energy exchanges between urban agglomerations and other places (i.e., areas that do not belong to urban agglomerations) are more extensive and deeper, posing difficulties for urban agglomerations to solve increasingly severe resources and environmental constraints (Huang et al, 2021;Miao et al, 2021), and making green economy of urban agglomerations more complicated than ever before.…”

Section: Introductionmentioning

confidence: 99%

Polycentric or monocentric, which kind of spatial structure is better for promoting the green economy? Evidence from Chinese urban agglomerations

Huang

Liao

2021

Environ Sci Pollut Res

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The green economy has gained worldwide attention, especially in the urban agglomerations where population and economic activities are highly concentrated. However, what kind of urban agglomeration spatial structure is more conducive to promoting the green economy? No clear conclusions have been made. Here, we study the impact of urban agglomeration spatial structure on the green economy, and also reveal how urban agglomeration spatial structure influences the three subsystems of green economy. We find that: (1) urban agglomeration spatial structural evolution is closely related to green economy, while in the research period, most urban agglomerations are not located in the optimal range of the spatial structure that drives the green economy. (2) Towards polycentric spatial structure is contributive to green economic growth, however, the excessively polycentric could not benefit green economy. (3) The evolution of urban agglomeration spatial structure exerts heterogenous impacts on the three subsystems when green economy is decomposed into economic subsystem, resources subsystem, and environmental subsystem. Towards polycentric is more conducive to the improvement of economic subsystem and resource subsystem, while, the tendency to monocentric drives the environmental subsystem development. (4) Lastly, our conclusions enlighten the urban agglomeration development planning and spatial mode for approaching a better performance in green economy.

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Spatio-temporal trends and influencing factors of PM2.5 concentrations in urban agglomerations in China between 2000 and 2016

Cited by 47 publications

References 50 publications

Spatial Differences and Influential Factors of Urban Carbon Emissions in China under the Target of Carbon Neutrality

Spatial Differences and Influential Factors of Urban Carbon Emissions in China under the Target of Carbon Neutrality

Assessing urban atmospheric environmental efficiency and factors influencing it in China

Polycentric or monocentric, which kind of spatial structure is better for promoting the green economy? Evidence from Chinese urban agglomerations

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