High-resolution temporal and spatial evolution of carbon emissions from building operations in Beijing

• Published in: Journal of cleaner production 2022-11, Vol.376, p.134272, Article 134272
• Main Authors: Wang, Jingjing, Wei, Jiajia, Zhang, Weirong, Liu, Zhansheng, Du, Xiuli, Liu, Wenxiang, Pan, Ke
• Format: Article
• Language: English
• Subjects: Building operations; Carbon emissions; Geographic information system; High resolution; Point of interest; Temporal and spatial distribution
• ISSN: 0959-6526; 1879-1786
• DOI: 10.1016/j.jclepro.2022.134272

Building, a main greenhouse gas source, accounts for approximately 38% of all carbon emissions, and whole-life-cycle carbon emissions from building operations account for 60%–80% of construction-related emissions. However, the high-resolution spatiotemporal distribution of building-operation carbon emissions is unclear. We developed a top-down method to calculate and map carbon emissions by (1) compiling a building-operations carbon-emission inventory that includes direct energy consumption and calculating the carbon emissions of four key energy-intensive sectors via an emissions-factor approach; (2) cleaning, reclassifying and assigning weights to POI data obtained from AutoNavi; and (3) using a GIS to obtain sectoral carbon emissions in each grid according to the POI weights and to overlay and map the total carbon emissions. In Beijing, overall carbon emissions peaked at 95.87 million tons (2012). Emissions showed a clustered core with a multicenter discrete radial distribution pattern. Additionally, the carbon emissions intensity peaked at 487.44 kg/m2 (2015). The per-unit-grid carbon emissions distribution range was generally normally distributed, and the range of 103–105 kg/grid had the widest distribution and largest number of associated grids. Southeastern carbon emissions hotspots developed, and cold spots appeared along the periphery. This research could help planners quickly zone and reduce carbon emissions, leading to improved future development strategies. •A top-down method is developed for mapping carbon emissions.•POI data are used to acquire geographical and attribute information of point sources.•A GIS platform is used to analyze the carbon-emission distribution characteristics.•Four key energy-intensive sectors are used to calculate carbon emissions.