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

TitleMapping Building-Based Spatiotemporal Distributions of Carbon Dioxide Emission: A Case Study in England
Authors
Keywordsbuilding-based
CO emissions 2
linear regression analysis
radiance-calibrated nightlight
Issue Date2022
Citation
International Journal of Environmental Research and Public Health, 2022, v. 19, n. 10, article no. 5986 How to Cite?
AbstractThe spatiotemporal inventory of carbon dioxide (CO2) 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 CO2 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 CO2 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 CO2 emissions at the building scale by integrating the Emissions Database for Global Atmospheric Research, building data, and Visible Infrared Imaging Radiometer Suite nighttime 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 CO2 emissions compared to grid-based emission maps. In addition, we observed seasonal differences in CO2 emissions. Specifically, buildings emitted significantly more CO2 in winter than in summer in England. We believe our results have great potential for use in carbon neutrality policy making and climate monitoring.
Persistent Identifierhttp://hdl.handle.net/10722/330797
ISSN
2019 Impact Factor: 2.849
2023 SCImago Journal Rankings: 0.808
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorZheng, Yue-
dc.contributor.authorOu, Jinpei-
dc.contributor.authorChen, Guangzhao-
dc.contributor.authorWu, Xinxin-
dc.contributor.authorLiu, Xiaoping-
dc.date.accessioned2023-09-05T12:14:30Z-
dc.date.available2023-09-05T12:14:30Z-
dc.date.issued2022-
dc.identifier.citationInternational Journal of Environmental Research and Public Health, 2022, v. 19, n. 10, article no. 5986-
dc.identifier.issn1661-7827-
dc.identifier.urihttp://hdl.handle.net/10722/330797-
dc.description.abstractThe spatiotemporal inventory of carbon dioxide (CO2) 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 CO2 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 CO2 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 CO2 emissions at the building scale by integrating the Emissions Database for Global Atmospheric Research, building data, and Visible Infrared Imaging Radiometer Suite nighttime 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 CO2 emissions compared to grid-based emission maps. In addition, we observed seasonal differences in CO2 emissions. Specifically, buildings emitted significantly more CO2 in winter than in summer in England. We believe our results have great potential for use in carbon neutrality policy making and climate monitoring.-
dc.languageeng-
dc.relation.ispartofInternational Journal of Environmental Research and Public Health-
dc.subjectbuilding-based-
dc.subjectCO emissions 2-
dc.subjectlinear regression analysis-
dc.subjectradiance-calibrated nightlight-
dc.titleMapping Building-Based Spatiotemporal Distributions of Carbon Dioxide Emission: A Case Study in England-
dc.typeArticle-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.3390/ijerph19105986-
dc.identifier.pmid35627524-
dc.identifier.scopuseid_2-s2.0-85129899414-
dc.identifier.volume19-
dc.identifier.issue10-
dc.identifier.spagearticle no. 5986-
dc.identifier.epagearticle no. 5986-
dc.identifier.eissn1660-4601-
dc.identifier.isiWOS:000802497300001-

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