Evaluating the impact of the COVID-19 pandemic on the geospatial distribution of buildings' carbon footprints associated with electricity consumption
The carbon footprint (CF) linked to electricity consumption in buildings has become a significant environmental issue because of its significant role in greenhouse gas emissions. This study seeks to assess and examine the CF of electricity consumption in buildings across various building types. Addi...
محفوظ في:
| المؤلف الرئيسي: | |
|---|---|
| مؤلفون آخرون: | , , , |
| التنسيق: | article |
| منشور في: |
2024
|
| الموضوعات: | |
| الوصول للمادة أونلاين: | http://dx.doi.org/10.1016/j.esr.2024.101350 https://www.sciencedirect.com/science/article/pii/S2211467X24000579 http://hdl.handle.net/10576/55711 |
| الوسوم: |
إضافة وسم
لا توجد وسوم, كن أول من يضع وسما على هذه التسجيلة!
|
| _version_ | 1857415084319440896 |
|---|---|
| author | Esmat, Zaidan |
| author2 | Abulibdeh, Ammar Jabbar, Rateb Onat, Nuri Cihat Kucukvar, Murat |
| author2_role | author author author author |
| author_facet | Esmat, Zaidan Abulibdeh, Ammar Jabbar, Rateb Onat, Nuri Cihat Kucukvar, Murat |
| author_role | author |
| dc.creator.none.fl_str_mv | Esmat, Zaidan Abulibdeh, Ammar Jabbar, Rateb Onat, Nuri Cihat Kucukvar, Murat |
| dc.date.none.fl_str_mv | 2024-06-02T07:08:30Z 2024-03-10 |
| dc.format.none.fl_str_mv | application/pdf |
| dc.identifier.none.fl_str_mv | http://dx.doi.org/10.1016/j.esr.2024.101350 Zaidan, E., Abulibdeh, A., Jabbar, R., Onat, N. C., & Kucukvar, M. (2024). Evaluating the impact of the COVID-19 pandemic on the geospatial distribution of buildings' carbon footprints associated with electricity consumption. Energy Strategy Reviews, 52, 101350. 2211-467X https://www.sciencedirect.com/science/article/pii/S2211467X24000579 http://hdl.handle.net/10576/55711 52 2211-4688 |
| dc.language.none.fl_str_mv | en |
| dc.publisher.none.fl_str_mv | Elsevier |
| dc.rights.none.fl_str_mv | http://creativecommons.org/licenses/by/4.0/ info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Carbon footprint Buildings Machine-learning models Spatial analysis COVID-19 |
| dc.title.none.fl_str_mv | Evaluating the impact of the COVID-19 pandemic on the geospatial distribution of buildings' carbon footprints associated with electricity consumption |
| dc.type.none.fl_str_mv | Article info:eu-repo/semantics/publishedVersion info:eu-repo/semantics/article |
| description | The carbon footprint (CF) linked to electricity consumption in buildings has become a significant environmental issue because of its significant role in greenhouse gas emissions. This study seeks to assess and examine the CF of electricity consumption in buildings across various building types. Additionally, this paper aims to investigate the impact of the COVID-19 pandemic on the CF of buildings. The investigation involves a comparative analysis between the CF values observed and predicted during the years affected by the pandemic. Additionally, the study evaluates the influence of the pandemic on the accuracy of CF model predictions by employing three distinct machine-learning models. Spatial analyses were conducted to identify clustering patterns of CF and identify areas of both high and low CF concentrations within the study area. The findings demonstrate significant disparities in the CF of electricity consumption across distinct building types, with residential buildings emerging as the largest contributors to carbon emissions. Moreover, the pandemic has had a notable impact on CF patterns, leading to alterations in the areas identified as hotspots and cold spots during the pandemic years compared to the pre-pandemic period, based on building types. |
| eu_rights_str_mv | openAccess |
| format | article |
| id | qu_087d12e42131d7463df2d8b08731ed15 |
| identifier_str_mv | Zaidan, E., Abulibdeh, A., Jabbar, R., Onat, N. C., & Kucukvar, M. (2024). Evaluating the impact of the COVID-19 pandemic on the geospatial distribution of buildings' carbon footprints associated with electricity consumption. Energy Strategy Reviews, 52, 101350. 2211-467X 52 2211-4688 |
| language_invalid_str_mv | en |
| network_acronym_str | qu |
| network_name_str | Qatar University repository |
| oai_identifier_str | oai:qspace.qu.edu.qa:10576/55711 |
| publishDate | 2024 |
| publisher.none.fl_str_mv | Elsevier |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | http://creativecommons.org/licenses/by/4.0/ |
| spelling | Evaluating the impact of the COVID-19 pandemic on the geospatial distribution of buildings' carbon footprints associated with electricity consumptionEsmat, ZaidanAbulibdeh, AmmarJabbar, RatebOnat, Nuri CihatKucukvar, MuratCarbon footprintBuildingsMachine-learning modelsSpatial analysisCOVID-19The carbon footprint (CF) linked to electricity consumption in buildings has become a significant environmental issue because of its significant role in greenhouse gas emissions. This study seeks to assess and examine the CF of electricity consumption in buildings across various building types. Additionally, this paper aims to investigate the impact of the COVID-19 pandemic on the CF of buildings. The investigation involves a comparative analysis between the CF values observed and predicted during the years affected by the pandemic. Additionally, the study evaluates the influence of the pandemic on the accuracy of CF model predictions by employing three distinct machine-learning models. Spatial analyses were conducted to identify clustering patterns of CF and identify areas of both high and low CF concentrations within the study area. The findings demonstrate significant disparities in the CF of electricity consumption across distinct building types, with residential buildings emerging as the largest contributors to carbon emissions. Moreover, the pandemic has had a notable impact on CF patterns, leading to alterations in the areas identified as hotspots and cold spots during the pandemic years compared to the pre-pandemic period, based on building types.This publication was made possible by an NPRP award [ NPRP13S- 0206–200272 ] from the Qatar National Research Fund (a member of Qatar Foundation). The open access publication of this article was funded by the Qatar National Library (QNL).Elsevier2024-06-02T07:08:30Z2024-03-10Articleinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://dx.doi.org/10.1016/j.esr.2024.101350Zaidan, E., Abulibdeh, A., Jabbar, R., Onat, N. C., & Kucukvar, M. (2024). Evaluating the impact of the COVID-19 pandemic on the geospatial distribution of buildings' carbon footprints associated with electricity consumption. Energy Strategy Reviews, 52, 101350.2211-467Xhttps://www.sciencedirect.com/science/article/pii/S2211467X24000579http://hdl.handle.net/10576/55711522211-4688enhttp://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccessoai:qspace.qu.edu.qa:10576/557112024-07-23T15:53:52Z |
| spellingShingle | Evaluating the impact of the COVID-19 pandemic on the geospatial distribution of buildings' carbon footprints associated with electricity consumption Esmat, Zaidan Carbon footprint Buildings Machine-learning models Spatial analysis COVID-19 |
| status_str | publishedVersion |
| title | Evaluating the impact of the COVID-19 pandemic on the geospatial distribution of buildings' carbon footprints associated with electricity consumption |
| title_full | Evaluating the impact of the COVID-19 pandemic on the geospatial distribution of buildings' carbon footprints associated with electricity consumption |
| title_fullStr | Evaluating the impact of the COVID-19 pandemic on the geospatial distribution of buildings' carbon footprints associated with electricity consumption |
| title_full_unstemmed | Evaluating the impact of the COVID-19 pandemic on the geospatial distribution of buildings' carbon footprints associated with electricity consumption |
| title_short | Evaluating the impact of the COVID-19 pandemic on the geospatial distribution of buildings' carbon footprints associated with electricity consumption |
| title_sort | Evaluating the impact of the COVID-19 pandemic on the geospatial distribution of buildings' carbon footprints associated with electricity consumption |
| topic | Carbon footprint Buildings Machine-learning models Spatial analysis COVID-19 |
| url | http://dx.doi.org/10.1016/j.esr.2024.101350 https://www.sciencedirect.com/science/article/pii/S2211467X24000579 http://hdl.handle.net/10576/55711 |