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.

Relating carbon and energy intensity of best-performing retailers with policy, strategy and building practice

“…Best-performing retailers' carbon and energy intensities CI and EI benchmarks positioned best practice energy performance under 346 kWh/m 2 /y and 115 kg CO 2 eq/ m 2 /y for food retailers and under 146 kWh/m 2 /y and 70 kg CO 2 eq/m 2 /y for non-food retailers (Ferreira et al 2018a(Ferreira et al , 2018b.…”

“…These results suggest that EI variability within in European countries is lower than that of other continents. Nevertheless, the fact that within Europe, stores located in countries with different climate conditions, have similar EI values, points to the existence of other more important factors affecting the EI than climate conditions (Ferreira et al 2018a(Ferreira et al , 2018b, such as location, opening hours, occupancy, staff behaviour, maintenance routines, store size, store volume, the nature of products being retailed, type, building age, equipment's efficiency (Spyrou et al 2014) or even local government regulation or building incentives.…”

“…In step 1 (CI and EI benchmarking), the context for EI and CI ranking was formulated, following the methodology of Ferreira et al (2018aFerreira et al ( , 2018b. Here, the EI and CI of the stores of the 250 highest revenue retailers were analysed based on publicly available data from the fiscal year 2016 (Deloitte and Stores Media 2016), to establish best practice reference levels for these two variables.…”

“…The term "entries" refers to aggregated EI or CI data for either a retailer's overall performance, or for a retailers' brand or country of operation. EI and CI data were published in Data in Brief article (Ferreira et al 2018a(Ferreira et al , 2018b. CI was reported less frequently by retailers than EI (particularly for non-food retailers); hence, the different counts between CI and EI.…”

“…Fundamentally, the value of benchmark stands in comparing one's business processes and performance metrics to industry's best practices, in the quest for increased performance (Wireman 2004). As no official definition of best practice benchmarks existed within the retail sector (Galvez-Martos et al 2013), the methodology proposed by Ferreira et al (2018aFerreira et al ( , 2018b to set best performance benchmarks is based on previous works developed for other sectors (de Fátima Castro et al 2015) and defined the threshold of the 25% bestperforming retailers within the sector, as the best practice both for EI and CI. Hence, best-performing food and non-food retailers were identified applying the mathematical operation first quartile (Q1), which set them at the upper limit of the first quartile, equivalent to the boundary of the 25% lowest values.…”