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Carbon (CI) and energy intensity (EI) dataset for retail stores
This data article presents data collected from the 250 highest revenue retailers around the world, assessed according to publicly available data from the fiscal year 2016, in order to determine retailer׳s overall carbon intensity (CI) and energy intensity (EI). Data collection included additional va...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6231287/ https://www.ncbi.nlm.nih.gov/pubmed/30456253 http://dx.doi.org/10.1016/j.dib.2018.10.080 |
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author | Ferreira, Ana Pinheiro, Manuel Duarte de Brito, Jorge Mateus, Ricardo |
author_facet | Ferreira, Ana Pinheiro, Manuel Duarte de Brito, Jorge Mateus, Ricardo |
author_sort | Ferreira, Ana |
collection | PubMed |
description | This data article presents data collected from the 250 highest revenue retailers around the world, assessed according to publicly available data from the fiscal year 2016, in order to determine retailer׳s overall carbon intensity (CI) and energy intensity (EI). Data collection included additional variables such as retailers’ revenue rank, operational typology, number of stores, store sales area and number of workers. Based on this dataset, CI and EI benchmarks were calculated for food and non-food retailers, applying the statistic function first quartile (Q1) for the best practice, second (Q2) and third (Q3) quartiles for conventional practice and fourth quartile (Q4) for worst practice and correlations were tested between the variables "EI", "CI" and "retailer revenue", applying the statistic function CORREL (Ferreira et al., In press) [1]. Finally, a cluster analysis was performed for food and non-food retailers, to identify possible segmentation patterns between the variables “EI”, “CI” and “retailer revenue”. The information provided in this data article is useful for furthering research developments on the influence of isolated variables on retail EI and CI and in assisting retailers, architects, engineers, and policy makers in establishing optimal energy performance goals for the design and operation of retail stores. For further data interpretation and discussion, see the article “Combined carbon and energy intensity benchmarks for sustainable retail stores” (Ferreira et al., In press), of the same authors. |
format | Online Article Text |
id | pubmed-6231287 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | Elsevier |
record_format | MEDLINE/PubMed |
spelling | pubmed-62312872018-11-19 Carbon (CI) and energy intensity (EI) dataset for retail stores Ferreira, Ana Pinheiro, Manuel Duarte de Brito, Jorge Mateus, Ricardo Data Brief Energy This data article presents data collected from the 250 highest revenue retailers around the world, assessed according to publicly available data from the fiscal year 2016, in order to determine retailer׳s overall carbon intensity (CI) and energy intensity (EI). Data collection included additional variables such as retailers’ revenue rank, operational typology, number of stores, store sales area and number of workers. Based on this dataset, CI and EI benchmarks were calculated for food and non-food retailers, applying the statistic function first quartile (Q1) for the best practice, second (Q2) and third (Q3) quartiles for conventional practice and fourth quartile (Q4) for worst practice and correlations were tested between the variables "EI", "CI" and "retailer revenue", applying the statistic function CORREL (Ferreira et al., In press) [1]. Finally, a cluster analysis was performed for food and non-food retailers, to identify possible segmentation patterns between the variables “EI”, “CI” and “retailer revenue”. The information provided in this data article is useful for furthering research developments on the influence of isolated variables on retail EI and CI and in assisting retailers, architects, engineers, and policy makers in establishing optimal energy performance goals for the design and operation of retail stores. For further data interpretation and discussion, see the article “Combined carbon and energy intensity benchmarks for sustainable retail stores” (Ferreira et al., In press), of the same authors. Elsevier 2018-11-02 /pmc/articles/PMC6231287/ /pubmed/30456253 http://dx.doi.org/10.1016/j.dib.2018.10.080 Text en © 2018 The Authors http://creativecommons.org/licenses/by/4.0/ This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Energy Ferreira, Ana Pinheiro, Manuel Duarte de Brito, Jorge Mateus, Ricardo Carbon (CI) and energy intensity (EI) dataset for retail stores |
title | Carbon (CI) and energy intensity (EI) dataset for retail stores |
title_full | Carbon (CI) and energy intensity (EI) dataset for retail stores |
title_fullStr | Carbon (CI) and energy intensity (EI) dataset for retail stores |
title_full_unstemmed | Carbon (CI) and energy intensity (EI) dataset for retail stores |
title_short | Carbon (CI) and energy intensity (EI) dataset for retail stores |
title_sort | carbon (ci) and energy intensity (ei) dataset for retail stores |
topic | Energy |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6231287/ https://www.ncbi.nlm.nih.gov/pubmed/30456253 http://dx.doi.org/10.1016/j.dib.2018.10.080 |
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