Spatiotemporal patterns and driving mechanism of carbon emissions in China's urban residential building sector. (15th January 2023)

• Record Type: Journal Article
• Authors: Chen, Huadun; Du, Qianxi; Huo, Tengfei; Liu, Peiran; Cai, Weiguang; Liu, Bingsheng
• Is Part Of: Energy. Volume 263:Part E(2023)
• Publication Date: 2023-01-15
• Subjects: Urban residential building sector -- Carbon emissions -- Temporal-spatial LMDI analysis Model -- Spatiotemporal pattern -- Driving forces; Power resources -- Periodicals; Power (Mechanics) -- Periodicals; Energy consumption -- Periodicals; 333.7905
• DOI: 10.1016/j.energy.2022.126102
• Languages: English
• ISSNs: 0360-5442

Abstract

Abstract: Revealing the regional differences and drivers of provincial urban residential building sector carbon emissions (URBCE) is critical for regional collaborative emission reduction in achieving the "Dural carbon" targets. This study tries to innovatively construct a framework for the exploration of the spatiotemporal patterns and driving mechanism of Chinese URBCE in 30 provinces during 2000–2019, combining kernel density estimation, spatial autocorrelation analysis and temporal-spatial LMDI model. Results indicate that: (1) A vast majority of regions have undergone a sharp growth in URBCE, with the regional gap of URBCE gradually widening. (2) Besides, the spatial pattern of regional URBCE is basically stable, with High-High distributed in the Northeast and North China, and Low-Low concentrated in the Southwest. (3) Urban residential building floor space per capita, population, and urbanization are promotive driving forces, while carbon intensity and energy intensity are increasingly inhibiting. This study can provide policymakers a better understanding of URBCE and help them develop regionally-specific emission reduction strategies. Additionally, it offers fresh perspective on how to research the spatiotemporal disparities of carbon emissions in other sectors and nations, and could be of international significance. Graphical abstract: Image 1 Highlights: Establishes a framework to explore spatiotemporal patterns of provincial URBCE. Investigates the spatial characteristics and dynamic evolution of regional URBCE. Adopts temporal-spatial LMDI model to uncover the driving mechanism on URBCE. A vast majority of regions have seen a sharp growth of URBCE during 2000–2019. Floor space per capita, population, and urbanization are promotive drivers to the URBCE.