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

Spatiotemporal monitoring of urban CO₂ 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 R² 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.