Mapping Building-Based Spatiotemporal Distributions of Carbon Dioxide Emission: A Case Study in England

Yue Zheng, Jinpei Ou, Guangzhao Chen, Xinxin Wu and Xiaoping Liu
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Yue Zheng: Guangdong Key Laboratory for Urbanization and Geo-Simulation, School of Geography and Planning, Sun Yat-sen University, Guangzhou 510275, China
Jinpei Ou: Guangdong Key Laboratory for Urbanization and Geo-Simulation, School of Geography and Planning, Sun Yat-sen University, Guangzhou 510275, China
Guangzhao Chen: Faculty of Architecture, The University of Hong Kong, Pokfulam, Hong Kong SAR, China
Xinxin Wu: Guangdong Key Laboratory for Urbanization and Geo-Simulation, School of Geography and Planning, Sun Yat-sen University, Guangzhou 510275, China
Xiaoping Liu: Guangdong Key Laboratory for Urbanization and Geo-Simulation, School of Geography and Planning, Sun Yat-sen University, Guangzhou 510275, China

IJERPH, 2022, vol. 19, issue 10, 1-22

Abstract: The spatiotemporal inventory of carbon dioxide (CO 2 ) emissions from the building sector is significant for formulating regional and global warming mitigation policies. Previous studies have attempted to use energy consumption models associated with field investigations to estimate CO 2 emissions from buildings at local scales, or they used spatial proxies to downscale emission sources from large geographic units to grid cells for larger scales. However, mapping the spatiotemporal distributions of CO 2 emissions on a large scale based on buildings remains challenging. Hence, we conducted a case study in England in 2015, wherein we developed linear regression models to analyze monthly CO 2 emissions at the building scale by integrating the Emissions Database for Global Atmospheric Research, building data, and Visible Infrared Imaging Radiometer Suite night-time lights images. The results showed that the proposed model that considered building data and night-time light imagery achieved the best fit. Fine-scale spatial heterogeneity was observed in the distributions of building-based CO 2 emissions compared to grid-based emission maps. In addition, we observed seasonal differences in CO 2 emissions. Specifically, buildings emitted significantly more CO 2 in winter than in summer in England. We believe our results have great potential for use in carbon neutrality policy making and climate monitoring.

Keywords: CO 2 emissions; linear regression analysis; radiance-calibrated nightlight; building-based (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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