Downscaling Aggregate Urban Metabolism Accounts to Local Districts
Autor(a) principal: | |
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Data de Publicação: | 2017 |
Outros Autores: | |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://repositorio.inesctec.pt/handle/123456789/6052 http://dx.doi.org/10.1111/jiec.12428 |
Resumo: | Urban metabolism accounts of total annual energy, water, and other resource flows are increasingly available for a variety of world cities. For local decision makers, however, it may be important to understand the variations of resource consumption within the city. Given the difficulty of gathering suburban resource consumption data for many cities, this article investigates the potential of statistical downscaling methods to estimate local resource consumption using socioeconomic or other data sources. We evaluate six classes of downscaling methods: ratio-based normalization; linear regression (both internally and externally calibrated); linear regression with spatial autocorrelation; multilevel linear regression; and a basic Bayesian analysis. The methods were applied to domestic energy consumption in London, UK, and our results show that it is possible to downscale aggregate resource consumption to smaller geographies with an average absolute prediction error of around 20%; however, performance varies widely by method, geography size, and fuel type. We also show how mapping these results can quickly identify districts with noteworthy resource consumption profiles. Further work should explore the design of local data collection strategies to enhance these methods and apply the techniques to other urban resources such as water or waste. |
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Downscaling Aggregate Urban Metabolism Accounts to Local DistrictsUrban metabolism accounts of total annual energy, water, and other resource flows are increasingly available for a variety of world cities. For local decision makers, however, it may be important to understand the variations of resource consumption within the city. Given the difficulty of gathering suburban resource consumption data for many cities, this article investigates the potential of statistical downscaling methods to estimate local resource consumption using socioeconomic or other data sources. We evaluate six classes of downscaling methods: ratio-based normalization; linear regression (both internally and externally calibrated); linear regression with spatial autocorrelation; multilevel linear regression; and a basic Bayesian analysis. The methods were applied to domestic energy consumption in London, UK, and our results show that it is possible to downscale aggregate resource consumption to smaller geographies with an average absolute prediction error of around 20%; however, performance varies widely by method, geography size, and fuel type. We also show how mapping these results can quickly identify districts with noteworthy resource consumption profiles. Further work should explore the design of local data collection strategies to enhance these methods and apply the techniques to other urban resources such as water or waste.2018-01-14T15:08:46Z2017-01-01T00:00:00Z2017info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://repositorio.inesctec.pt/handle/123456789/6052http://dx.doi.org/10.1111/jiec.12428engIsabel HortaKeirstead,Jinfo:eu-repo/semantics/embargoedAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2023-05-15T10:20:04Zoai:repositorio.inesctec.pt:123456789/6052Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T17:52:38.148280Repositório 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 |
Downscaling Aggregate Urban Metabolism Accounts to Local Districts |
title |
Downscaling Aggregate Urban Metabolism Accounts to Local Districts |
spellingShingle |
Downscaling Aggregate Urban Metabolism Accounts to Local Districts Isabel Horta |
title_short |
Downscaling Aggregate Urban Metabolism Accounts to Local Districts |
title_full |
Downscaling Aggregate Urban Metabolism Accounts to Local Districts |
title_fullStr |
Downscaling Aggregate Urban Metabolism Accounts to Local Districts |
title_full_unstemmed |
Downscaling Aggregate Urban Metabolism Accounts to Local Districts |
title_sort |
Downscaling Aggregate Urban Metabolism Accounts to Local Districts |
author |
Isabel Horta |
author_facet |
Isabel Horta Keirstead,J |
author_role |
author |
author2 |
Keirstead,J |
author2_role |
author |
dc.contributor.author.fl_str_mv |
Isabel Horta Keirstead,J |
description |
Urban metabolism accounts of total annual energy, water, and other resource flows are increasingly available for a variety of world cities. For local decision makers, however, it may be important to understand the variations of resource consumption within the city. Given the difficulty of gathering suburban resource consumption data for many cities, this article investigates the potential of statistical downscaling methods to estimate local resource consumption using socioeconomic or other data sources. We evaluate six classes of downscaling methods: ratio-based normalization; linear regression (both internally and externally calibrated); linear regression with spatial autocorrelation; multilevel linear regression; and a basic Bayesian analysis. The methods were applied to domestic energy consumption in London, UK, and our results show that it is possible to downscale aggregate resource consumption to smaller geographies with an average absolute prediction error of around 20%; however, performance varies widely by method, geography size, and fuel type. We also show how mapping these results can quickly identify districts with noteworthy resource consumption profiles. Further work should explore the design of local data collection strategies to enhance these methods and apply the techniques to other urban resources such as water or waste. |
publishDate |
2017 |
dc.date.none.fl_str_mv |
2017-01-01T00:00:00Z 2017 2018-01-14T15:08:46Z |
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://repositorio.inesctec.pt/handle/123456789/6052 http://dx.doi.org/10.1111/jiec.12428 |
url |
http://repositorio.inesctec.pt/handle/123456789/6052 http://dx.doi.org/10.1111/jiec.12428 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
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info:eu-repo/semantics/embargoedAccess |
eu_rights_str_mv |
embargoedAccess |
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application/pdf |
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Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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RCAAP |
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RCAAP |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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