Evaluating the impact of the COVID-19 pandemic on the geospatial distribution of buildings' carbon footprints associated with electricity consumption
<p>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 ty...
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2024
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| _version_ | 1864513509814960128 |
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| author | Esmat Zaidan (16855203) |
| author2 | Ammar Abulibdeh (15785928) Rateb Jabbar (16946565) Nuri Cihat Onat (11190245) Murat Kucukvar (11190248) |
| author2_role | author author author author |
| author_facet | Esmat Zaidan (16855203) Ammar Abulibdeh (15785928) Rateb Jabbar (16946565) Nuri Cihat Onat (11190245) Murat Kucukvar (11190248) |
| author_role | author |
| dc.creator.none.fl_str_mv | Esmat Zaidan (16855203) Ammar Abulibdeh (15785928) Rateb Jabbar (16946565) Nuri Cihat Onat (11190245) Murat Kucukvar (11190248) |
| dc.date.none.fl_str_mv | 2024-03-10T09:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1016/j.esr.2024.101350 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/Evaluating_the_impact_of_the_COVID-19_pandemic_on_the_geospatial_distribution_of_buildings_carbon_footprints_associated_with_electricity_consumption/26403961 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Environmental sciences Environmental management Information and computing sciences Machine learning 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 | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p>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.</p><h2>Other Information</h2> <p> Published in: Energy Strategy Reviews<br> License: <a href="http://creativecommons.org/licenses/by/4.0/" target="_blank">http://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1016/j.esr.2024.101350" target="_blank">https://dx.doi.org/10.1016/j.esr.2024.101350</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_6b40ef95eb559ffc1c36da87dc2b95a5 |
| identifier_str_mv | 10.1016/j.esr.2024.101350 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/26403961 |
| publishDate | 2024 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Evaluating the impact of the COVID-19 pandemic on the geospatial distribution of buildings' carbon footprints associated with electricity consumptionEsmat Zaidan (16855203)Ammar Abulibdeh (15785928)Rateb Jabbar (16946565)Nuri Cihat Onat (11190245)Murat Kucukvar (11190248)Environmental sciencesEnvironmental managementInformation and computing sciencesMachine learningCarbon footprintBuildingsMachine-learning modelsSpatial analysisCOVID-19<p>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.</p><h2>Other Information</h2> <p> Published in: Energy Strategy Reviews<br> License: <a href="http://creativecommons.org/licenses/by/4.0/" target="_blank">http://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1016/j.esr.2024.101350" target="_blank">https://dx.doi.org/10.1016/j.esr.2024.101350</a></p>2024-03-10T09:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1016/j.esr.2024.101350https://figshare.com/articles/journal_contribution/Evaluating_the_impact_of_the_COVID-19_pandemic_on_the_geospatial_distribution_of_buildings_carbon_footprints_associated_with_electricity_consumption/26403961CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/264039612024-03-10T09:00:00Z |
| spellingShingle | Evaluating the impact of the COVID-19 pandemic on the geospatial distribution of buildings' carbon footprints associated with electricity consumption Esmat Zaidan (16855203) Environmental sciences Environmental management Information and computing sciences Machine learning 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 | Environmental sciences Environmental management Information and computing sciences Machine learning Carbon footprint Buildings Machine-learning models Spatial analysis COVID-19 |