A new composite indicator for assessing energy poverty using normalized entropy

Detalhes bibliográficos
Autor(a) principal: Macedo, Pedro
Data de Publicação: 2022
Outros Autores: Madaleno, Mara, Moutinho, Victor
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10773/35241
Resumo: Using a unique or common measure of energy poverty is very limited for the true classification of a household being in energy poverty. Thus, this study proposes a composite indicator, whose weights will be determined from the estimation of two relationships using a robust and stable methodology based on information theory. This work considers two regression models, where the two dependent variables are the gross domestic product and greenhouse gas, and the 12 energy poverty explanatory variables are based on those proposed by Recalde et al. (2019), for the period 2008-2018. Hence, the study presents a more comprehensive measurement with additional dimensions, weights, and indicators. Probably most important, in addition to the discussed proposal with a specific choice of models and variables, this work reveals a promising methodology that can be replicated in any other theoretical configuration. This approach is suitable for the discussion and design of new energy, environmental and social policies. Findings can be used to assess in advance the effectiveness of energy poverty measures, turning the model into a valuable policy tool.
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spelling A new composite indicator for assessing energy poverty using normalized entropyEuropean CountriesIndexInfo-MetricsMaximum EntropyRegression AnalysisUsing a unique or common measure of energy poverty is very limited for the true classification of a household being in energy poverty. Thus, this study proposes a composite indicator, whose weights will be determined from the estimation of two relationships using a robust and stable methodology based on information theory. This work considers two regression models, where the two dependent variables are the gross domestic product and greenhouse gas, and the 12 energy poverty explanatory variables are based on those proposed by Recalde et al. (2019), for the period 2008-2018. Hence, the study presents a more comprehensive measurement with additional dimensions, weights, and indicators. Probably most important, in addition to the discussed proposal with a specific choice of models and variables, this work reveals a promising methodology that can be replicated in any other theoretical configuration. This approach is suitable for the discussion and design of new energy, environmental and social policies. Findings can be used to assess in advance the effectiveness of energy poverty measures, turning the model into a valuable policy tool.Springer2022-11-22T09:57:53Z2022-01-01T00:00:00Z2022info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10773/35241eng0303-830010.1007/s11205-022-02938-1Macedo, PedroMadaleno, MaraMoutinho, Victorinfo: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:RCAAP2024-02-22T12:07:46Zoai:ria.ua.pt:10773/35241Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:06:16.260223Repositório 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 A new composite indicator for assessing energy poverty using normalized entropy
title A new composite indicator for assessing energy poverty using normalized entropy
spellingShingle A new composite indicator for assessing energy poverty using normalized entropy
Macedo, Pedro
European Countries
Index
Info-Metrics
Maximum Entropy
Regression Analysis
title_short A new composite indicator for assessing energy poverty using normalized entropy
title_full A new composite indicator for assessing energy poverty using normalized entropy
title_fullStr A new composite indicator for assessing energy poverty using normalized entropy
title_full_unstemmed A new composite indicator for assessing energy poverty using normalized entropy
title_sort A new composite indicator for assessing energy poverty using normalized entropy
author Macedo, Pedro
author_facet Macedo, Pedro
Madaleno, Mara
Moutinho, Victor
author_role author
author2 Madaleno, Mara
Moutinho, Victor
author2_role author
author
dc.contributor.author.fl_str_mv Macedo, Pedro
Madaleno, Mara
Moutinho, Victor
dc.subject.por.fl_str_mv European Countries
Index
Info-Metrics
Maximum Entropy
Regression Analysis
topic European Countries
Index
Info-Metrics
Maximum Entropy
Regression Analysis
description Using a unique or common measure of energy poverty is very limited for the true classification of a household being in energy poverty. Thus, this study proposes a composite indicator, whose weights will be determined from the estimation of two relationships using a robust and stable methodology based on information theory. This work considers two regression models, where the two dependent variables are the gross domestic product and greenhouse gas, and the 12 energy poverty explanatory variables are based on those proposed by Recalde et al. (2019), for the period 2008-2018. Hence, the study presents a more comprehensive measurement with additional dimensions, weights, and indicators. Probably most important, in addition to the discussed proposal with a specific choice of models and variables, this work reveals a promising methodology that can be replicated in any other theoretical configuration. This approach is suitable for the discussion and design of new energy, environmental and social policies. Findings can be used to assess in advance the effectiveness of energy poverty measures, turning the model into a valuable policy tool.
publishDate 2022
dc.date.none.fl_str_mv 2022-11-22T09:57:53Z
2022-01-01T00:00:00Z
2022
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dc.type.driver.fl_str_mv info:eu-repo/semantics/article
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status_str publishedVersion
dc.identifier.uri.fl_str_mv http://hdl.handle.net/10773/35241
url http://hdl.handle.net/10773/35241
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 0303-8300
10.1007/s11205-022-02938-1
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eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Springer
publisher.none.fl_str_mv Springer
dc.source.none.fl_str_mv reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
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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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