Dataset Information

Carbon (CI) and energy intensity (EI) dataset for retail stores.

ABSTRACT: 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.

SUBMITTER: Ferreira A

PROVIDER: S-EPMC6231287 | biostudies-literature | 2018 Dec

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Carbon (CI) and energy intensity (EI) dataset for retail stores.

Ferreira Ana A Pinheiro Manuel Duarte MD de Brito Jorge J Mateus Ricardo R

Data in brief 20181102

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-fo ...[more]

PMID: 30456253

Similar Datasets

Project description:BackgroundThe consumer nutrition environment has been conceptualised as in-store environmental factors that influence food shopping habits. More healthful in-store environments could be characterised as those which promote healthful food choices such as selling good quality healthy foods or placing them in prominent locations to prompt purchasing. Research measuring the full-range of in-store environmental factors concurrently is limited.PurposeTo develop a summary score of 'healthfulness' composed of nine in-store factors that influence food shopping behaviour, and to assess this score by store type and neighbourhood deprivation.MethodsA cross-sectional survey of 601 retail food stores, including supermarkets, grocery stores and convenience stores, was completed in Hampshire, United Kingdom between July 2010 and June 2011. The survey measured nine variables (variety, price, quality, promotions, shelf placement, store placement, nutrition information, healthier alternatives and single fruit sale) to assess the healthfulness of retail food stores on seven healthy and five less healthy foods that are markers of diet quality. Four steps were completed to create nine individual variable scores and another three to create an overall score of healthfulness for each store.ResultsAnalysis of variance showed strong evidence of a difference in overall healthfulness by store type (p <?0.001). Large and premium supermarkets offered the most healthful shopping environments for consumers. Discount supermarkets, 'world', convenience and petrol stores offered less healthful environments to consumers however there was variation across the healthfulness spectrum. No relationship between overall healthfulness and neighbourhood deprivation was observed (p =?0.1).ConclusionsA new composite measure of nine variables that can influence food choices was developed to provide an overall assessment of the healthfulness of retail food stores. This composite score could be useful in future research to measure the relationship between main food store and quality of diet, and to evaluate the effects of multi-component food environment interventions.

Dataset Information

Carbon (CI) and energy intensity (EI) dataset for retail stores.

Publications

Carbon (CI) and energy intensity (EI) dataset for retail stores.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets