Exploring the effect of urban sprawl on carbon dioxide emissions: An urban sprawl model analysis from remotely sensed nighttime light data

Wu, Yizhen; Li, Chuanlong; Shi, Kaifang; Liu, Shirao; Chang, Zhijian

doi:10.1016/j.eiar.2021.106731

Cited by 55 publications

(11 citation statements)

References 58 publications

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“…The research on the impact of urbanization on the eco-environment is mainly concentrated on their interaction [28], mechanism [29], and coupling and coordination analyses [29]. Meanwhile, research on UL and CEs has gained attention over the years [33,34]. The measurement of UL mainly includes population, economic, spatial, and social urbanization [35,36], among which population urbanization is the most preferred.…”

Section: Introductionmentioning

confidence: 99%

Spatially Non-Stationary Response of Carbon Emissions to Urbanization in Han River Ecological Economic Belt, China

Zhen-wei

Mao

et al. 2022

IJERPH

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Within the context of the “30·60 dual carbon” goal, China’s low-carbon sustainable development is affected by a series of environmental problems caused by rapid urbanization. Revealing the impacts of urbanization on carbon emissions (CEs) is conducive to low-carbon city construction and green transformation, attracting the attention of scholars worldwide. The research is rich concerning the impacts of urbanization on CEs but lacking in studies on their spatial dependence and heterogeneity at multiple different scales, especially in areas with important ecological statuses, such as the Han River Ecological Economic Belt (HREEB) in China. To address these gaps, this study first constructed an urbanization level (UL) measurement method. Then, using a bivariate spatial autocorrelation analysis and geographically weighted regression model, the spatial relationships between UL and CEs from 2000 to 2020 were investigated from a multiscale perspective. The results were shown as follows. The total CEs in the HREEB witnessed an upsurge in the past two decades, which was mainly dispersed in the central urban areas of the HREEB. The ULs in different regions of the HREEB varied evidently, with high levels in the east and low levels in the central and western regions, while the overall UL in 2020 was higher than that in 2000, regardless of the research scale. During the study period, there was a significant, positive spatial autocorrelation between UL and CEs, and similar spatial distribution characteristics of the bivariate spatial autocorrelation between CEs and UL at different times, and different scales were observed. UL impacted CEs positively, but the impacts varied at different grid scales during the study period. The regression coefficients in 2020 were higher than those in 2000, but the spatial distribution was more scattered, and more detailed information was provided at the 5 km grid scale than at the 10 km grid scale. The findings of this research can advance policy enlightenment for low-carbon city construction and green transformation in HREEB and provide a reference for CE reduction in other similar regions of the world.

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Section: Introductionmentioning

confidence: 99%

Spatially Non-Stationary Response of Carbon Emissions to Urbanization in Han River Ecological Economic Belt, China

Zhen-wei

Mao

et al. 2022

IJERPH

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“…Some scholars have used a single indicator to reflect urban sprawl, while others have used multiple indicators to construct sprawl indices to measure sprawl characteristics. The use of a single indicator to measure urban sprawl focuses on the relationship between urban population and land area, mainly in terms of density (population density, employment density, residential density, density of residential units) (Terzi and Bolen, 2009;Chen et al, 2021), elasticity (land and population growth elasticity, land and employment growth elasticity, land and GDP growth elasticity) (Han, 2020; (Han, 2020;, spatial patterns (accessibility, agglomeration, connectivity, fragmentation) (Mustafa and Teller, 2020;Wu et al, 2022), and so on. In contrast, multidimensional indicators are more helpful in reflecting the true picture of urban sprawl.…”

Section: Measurement Of Urban Sprawlmentioning

confidence: 99%

The impact of urban sprawl on green total factor productivity: A spatial econometric analysis in China

Liu

Wu²

2023

Front. Environ. Sci.

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The rapid expansion of Chinese cities has led to serious urban productivity and eco-environment changes, and has therefore attracted considerable international academic attention. The main objective of this study is to investigate the theoretical mechanisms and practical effects of urban sprawl on green total factor productivity (GTFP), in order to provide a reference for optimizing the spatial layout of cities and promoting high-quality economic development. Realistic urban land area and population characteristics are extracted using DMSP/OLS and NPP/VIIRS nighttime lighting data, and LandScan global population dynamics statistics to measure the urban sprawl index. GTFP is measured using a super-SBM model that considers undesirable output. Based on the panel data of Chinese cities from 2006 to 2020, a spatial Durbin model was constructed to carry out the empirical analysis. The results show that, overall, urban sprawl in China is detrimental to its own GTFP, while contributing to the GTFP of neighboring cities. The impacts of urban sprawl vary markedly across cities of different sizes and across regions.

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“…Currently, there has been a wealth of research on the relationship between urban expansion and form changes and carbon emissions. These studies have revealed a more in-depth nonlinear relationship between the two [ 29 , 30 ]. While a large number of studies have explored the key factors that drive carbon emissions [ 31 ], and it is also recognized that urban form changes have an important impact on carbon emissions during urban expansion [ 32 ], there is a lack of understanding of the complex relationship between urban expansion and form changes and carbon emissions under the driving of multiple factors.…”

Section: Introductionmentioning

confidence: 99%

Dynamics of urban expansion and form changes impacting carbon emissions in the Guangdong-Hong Kong-Macao Greater Bay Area counties

Ming,

Wang,

Chen

et al. 2024

Heliyon

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Exploring the effect of urban sprawl on carbon dioxide emissions: An urban sprawl model analysis from remotely sensed nighttime light data

Cited by 55 publications

References 58 publications

Spatially Non-Stationary Response of Carbon Emissions to Urbanization in Han River Ecological Economic Belt, China

Spatially Non-Stationary Response of Carbon Emissions to Urbanization in Han River Ecological Economic Belt, China

The impact of urban sprawl on green total factor productivity: A spatial econometric analysis in China

Dynamics of urban expansion and form changes impacting carbon emissions in the Guangdong-Hong Kong-Macao Greater Bay Area counties

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