Modeling spatiotemporal carbon emissions for two mega-urban regions in China using urban form and panel data analysis.

Cai, Meng, Ren, Chao, Shi, Yuan ORCID: 0000-0003-4011-8735, Chen, Guangzhao, Xie, Jing and Ng, Edward (2022) Modeling spatiotemporal carbon emissions for two mega-urban regions in China using urban form and panel data analysis. The Science of the total environment, 857 (Pt 3). p. 159612.

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Official URL: http://dx.doi.org/10.1016/j.scitotenv.2022.159612

Abstract

Spatiotemporal monitoring of urban CO<sub>2</sub> emissions is crucial for developing strategies and actions to mitigate climate change. However, most spatiotemporal inventories do not adopt urban form data and have a coarse resolution of over 1 km, which limits their implications in intra-city planning. This study aims to model the spatiotemporal carbon emissions of the two largest mega-urban regions in China, the Yangtze River Delta and the Pearl River Delta, using urban form data from the Local Climate Zone scheme and landscape metrics, nighttime light images, and a year-fixed effects model at a fine resolution from 2012 to 2016. The panel data model has an R<sup>2</sup> value of 0.98. This study identifies an overall fall in carbon emissions in both regions since 2012 and a slight elevation of emissions from 2015 to 2016. In addition, urban compaction and integrated natural landscapes are found to be related to low emissions, whereas scattered low-rise buildings are associated with rising carbon emissions. Furthermore, this study more accurately extracts urban areas and can more clearly identify intra-urban variations in carbon emissions than other datasets. The open data supported methodology, regression models, and results can provide accurate and quantifiable evidence at the community level for achieving a carbon-neutral built environment.

Item Type:	Article
Uncontrolled Keywords:	Carbon emission, Local climate zone, NPP-VIIRS, Landscape metrics, Mega-urban regions, Built Environment
Depositing User:	Symplectic Admin
Date Deposited:	14 Nov 2022 10:22
Last Modified:	20 Oct 2023 01:30
DOI:	10.1016/j.scitotenv.2022.159612
Related URLs:	Author Publisher
URI:	https://livrepository.liverpool.ac.uk/id/eprint/3166164