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

TitleModeling spatiotemporal carbon emissions for two mega-urban regions in China using urban form and panel data analysis
Authors
KeywordsBuilt Environment
Carbon emission
Landscape metrics
Local climate zone
Mega-urban regions
NPP-VIIRS
Issue Date2023
Citation
Science of the Total Environment, 2023, v. 857, article no. 159612 How to Cite?
AbstractSpatiotemporal 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.
Persistent Identifierhttp://hdl.handle.net/10722/330867
ISSN
2023 Impact Factor: 8.2
2023 SCImago Journal Rankings: 1.998
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorCai, Meng-
dc.contributor.authorRen, Chao-
dc.contributor.authorShi, Yuan-
dc.contributor.authorChen, Guangzhao-
dc.contributor.authorXie, Jing-
dc.contributor.authorNg, Edward-
dc.date.accessioned2023-09-05T12:15:25Z-
dc.date.available2023-09-05T12:15:25Z-
dc.date.issued2023-
dc.identifier.citationScience of the Total Environment, 2023, v. 857, article no. 159612-
dc.identifier.issn0048-9697-
dc.identifier.urihttp://hdl.handle.net/10722/330867-
dc.description.abstractSpatiotemporal 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.-
dc.languageeng-
dc.relation.ispartofScience of the Total Environment-
dc.subjectBuilt Environment-
dc.subjectCarbon emission-
dc.subjectLandscape metrics-
dc.subjectLocal climate zone-
dc.subjectMega-urban regions-
dc.subjectNPP-VIIRS-
dc.titleModeling spatiotemporal carbon emissions for two mega-urban regions in China using urban form and panel data analysis-
dc.typeArticle-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1016/j.scitotenv.2022.159612-
dc.identifier.pmid36273567-
dc.identifier.scopuseid_2-s2.0-85140441078-
dc.identifier.volume857-
dc.identifier.spagearticle no. 159612-
dc.identifier.epagearticle no. 159612-
dc.identifier.eissn1879-1026-
dc.identifier.isiWOS:000906908100010-

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