Modeling national decarbonization capabilities using Kohonen maps
Autor(a) principal: | |
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Data de Publicação: | 2022 |
Outros Autores: | |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://hdl.handle.net/10071/28849 |
Resumo: | This study sought to develop a method to cluster countries based on their decarbonization capabilities and to determine how these nations’ reduction of carbon dioxide (CO2) emissions has evolved over time. CO2 emissions clusters were identified using 11 indicators that measure both direct and indirect CO2 emissions, differentiating countries by their economic and population growth, energy consumption, and CO2 emission level. The panel data included 39 countries over the 10-year period of 2012–2021. The clustering was based on such type of neural networks as Kohonen self-organizing maps. This type of model facilitated grouping countries by similar decarbonization capabilities and economic development. The findings reveal that Norway and Sweden are the leaders in creating climate-resilient economies among the 39 countries analyzed. The analysis carried out can help other countries establish benchmarks for improving their own internal decarbonization activities based on leader nations’ strategies and borrowing their best practices for more efficient results. This study thus contributes to the literature regarding decarbonization activities by offering a multi-country dynamic clustering method using Kohonen maps. |
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Modeling national decarbonization capabilities using Kohonen mapsCarbon dioxide (CO2)Emission targetDecarbonizationClusteringSelf-organizingmapNeural networkThis study sought to develop a method to cluster countries based on their decarbonization capabilities and to determine how these nations’ reduction of carbon dioxide (CO2) emissions has evolved over time. CO2 emissions clusters were identified using 11 indicators that measure both direct and indirect CO2 emissions, differentiating countries by their economic and population growth, energy consumption, and CO2 emission level. The panel data included 39 countries over the 10-year period of 2012–2021. The clustering was based on such type of neural networks as Kohonen self-organizing maps. This type of model facilitated grouping countries by similar decarbonization capabilities and economic development. The findings reveal that Norway and Sweden are the leaders in creating climate-resilient economies among the 39 countries analyzed. The analysis carried out can help other countries establish benchmarks for improving their own internal decarbonization activities based on leader nations’ strategies and borrowing their best practices for more efficient results. This study thus contributes to the literature regarding decarbonization activities by offering a multi-country dynamic clustering method using Kohonen maps.Kyiv National Economic University2023-06-30T15:43:54Z2022-01-01T00:00:00Z20222023-06-30T16:43:22Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10071/28849eng2415-351610.33111/nfmte.2022.003Zhytkevych, O.Brochado, A.info:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2023-11-09T17:41:57Zoai:repositorio.iscte-iul.pt:10071/28849Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T22:19:33.889345Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse |
dc.title.none.fl_str_mv |
Modeling national decarbonization capabilities using Kohonen maps |
title |
Modeling national decarbonization capabilities using Kohonen maps |
spellingShingle |
Modeling national decarbonization capabilities using Kohonen maps Zhytkevych, O. Carbon dioxide (CO2) Emission target Decarbonization Clustering Self-organizingmap Neural network |
title_short |
Modeling national decarbonization capabilities using Kohonen maps |
title_full |
Modeling national decarbonization capabilities using Kohonen maps |
title_fullStr |
Modeling national decarbonization capabilities using Kohonen maps |
title_full_unstemmed |
Modeling national decarbonization capabilities using Kohonen maps |
title_sort |
Modeling national decarbonization capabilities using Kohonen maps |
author |
Zhytkevych, O. |
author_facet |
Zhytkevych, O. Brochado, A. |
author_role |
author |
author2 |
Brochado, A. |
author2_role |
author |
dc.contributor.author.fl_str_mv |
Zhytkevych, O. Brochado, A. |
dc.subject.por.fl_str_mv |
Carbon dioxide (CO2) Emission target Decarbonization Clustering Self-organizingmap Neural network |
topic |
Carbon dioxide (CO2) Emission target Decarbonization Clustering Self-organizingmap Neural network |
description |
This study sought to develop a method to cluster countries based on their decarbonization capabilities and to determine how these nations’ reduction of carbon dioxide (CO2) emissions has evolved over time. CO2 emissions clusters were identified using 11 indicators that measure both direct and indirect CO2 emissions, differentiating countries by their economic and population growth, energy consumption, and CO2 emission level. The panel data included 39 countries over the 10-year period of 2012–2021. The clustering was based on such type of neural networks as Kohonen self-organizing maps. This type of model facilitated grouping countries by similar decarbonization capabilities and economic development. The findings reveal that Norway and Sweden are the leaders in creating climate-resilient economies among the 39 countries analyzed. The analysis carried out can help other countries establish benchmarks for improving their own internal decarbonization activities based on leader nations’ strategies and borrowing their best practices for more efficient results. This study thus contributes to the literature regarding decarbonization activities by offering a multi-country dynamic clustering method using Kohonen maps. |
publishDate |
2022 |
dc.date.none.fl_str_mv |
2022-01-01T00:00:00Z 2022 2023-06-30T15:43:54Z 2023-06-30T16:43:22Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://hdl.handle.net/10071/28849 |
url |
http://hdl.handle.net/10071/28849 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
2415-3516 10.33111/nfmte.2022.003 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
Kyiv National Economic University |
publisher.none.fl_str_mv |
Kyiv National Economic University |
dc.source.none.fl_str_mv |
reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação instacron:RCAAP |
instname_str |
Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
instacron_str |
RCAAP |
institution |
RCAAP |
reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
collection |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
repository.name.fl_str_mv |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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1799134755065692160 |