Exploring the effect of urban spatial development pattern on carbon dioxide emissions in China: A socioeconomic density distribution approach based on remotely sensed nighttime light data. (September 2022)

• Record Type: Journal Article
• Authors: Liu, Shirao; Shen, Jingwei; Liu, Guifen; Wu, Yizhen; Shi, Kaifang
• Is Part Of: Computers, environment and urban systems. Volume 96(2022)
• Publication Date: 2022-09
• Subjects: Urban spatial development pattern -- Carbon dioxide emissions -- Nighttime light data -- NPP-VIIRS -- Socioeconomic density distribution -- China; City planning -- Data processing -- Periodicals; Regional planning -- Data processing -- Periodicals; 303.4834
• DOI: 10.1016/j.compenvurbsys.2022.101847
• Languages: English
• ISSNs: 0198-9715

Abstract

Abstract: Exploring the effect of urban spatial development pattern (UPD) on carbon dioxide emissions (CDEs) (EUC) is important for understanding low-carbon sustainable development. Numerous studies on EUC have mainly focused on individual cities or regions within the mixed conclusions due to the lack of reliable UPD indices and reasonable methods. Thus, taking China's 257 prefecture-level cities as experimental objects, a novel system approach was developed from the perspective of socioeconomic density distribution (SED) index to measure UPD on the basis of the Suomi National Polar-orbiting Partnership (NPP) visible infrared imaging radiometer suite (VIIRS) nighttime light data. EUC was then analyzed on the basis of the dynamic panel data model from multiple perspectives. Results show that the SED index can effectively measure UPD with rich spatial information from multiple dimensions. The coefficients of SED and (SED) 2 are 0.129 and − 1.240, respectively, indicating that EUC shows a clear inverted U-shaped curve in China, i.e., an increase in UPD compactness increases CDEs at the beginning, and when a certain height is reached, an increase in UPD compactness decreases CDEs. Heterogeneity analysis indicates a U-shaped curve of EUC is found in megalopolis, and inverse U-shaped curve are observed in medium and small cities. Bus passenger volume, energy consumption, infrastructure, and housing demand are proven as the transmission factors of EUC. It is suggested that utilizing the positive externality effect of agglomeration and accelerating the inflection point of the inverse U-shaped curve may be necessary because the improvement of urban socioeconomic agglomeration will improve the UPD compactness and reduce CDEs. Highlights: The socioeconomic density distribution index can effectively measure urban spatial development pattern; EUC shows an obvious inverse "U-shaped" curve in China; A "U" curve of EUC is found in megalopolis, while inverse "U" curves are observed in medium and small cities; Bus passenger volume, energy consumption, infrastructure, and housing demand are transmission factors of EUC.