Water intensity benchmarks for sustainable retail stores
| Main Author: | Santos Ferreira, Ana Sofia |
|---|---|
| Format: | Dataset |
| Terbitan: |
Mendeley
, 2020
|
| Subjects: | |
| Online Access: |
https:/data.mendeley.com/datasets/byxt34g25h |
| ctrlnum |
0.17632-byxt34g25h.1 |
|---|---|
| fullrecord |
<?xml version="1.0"?>
<dc><creator>Santos Ferreira, Ana Sofia</creator><title>Water intensity benchmarks for sustainable retail stores</title><publisher>Mendeley</publisher><description>The table present retrieved data of the highest revenue retailers for the research paper entitled "Water Intensity benchmarks for sustainable retail stores", according to the variables: "Water Intensity (WI)", “Company”, “Country of origin”, ”Dominant operational category”, ”Store typology”, ”Number of countries of operation”, “Retail revenue”, “Number of stores”, ”Average store sales area”, ”Total store sales area”, “Water intensity”, ”Average number of workers per store”, “Total number of workers” and “Revenue per store sales area”.
Based on this data, a WI benchmark was performed, identifying average, minimum and maximum values for each food and non-food retail sub-type, and outliers were identified with the interquartile range and removed so as to reduce error in each category. A linear regression analysis was also performed in retail sub-types that had data from three or more retailers, in order to estimate the relationship between WI as a dependent variable and other independent variables. R-squared values (R²) were calculated for each independent variable, those scoring higher than 0.7 were considered to have a strong effect size on the prediction of WI.
</description><subject>Benchmarking</subject><subject>Water</subject><subject>Global Retailing</subject><subject>Retail Sector</subject><type>Other:Dataset</type><identifier>10.17632/byxt34g25h.1</identifier><rights>Creative Commons Attribution 4.0 International</rights><rights>http://creativecommons.org/licenses/by/4.0</rights><relation>https:/data.mendeley.com/datasets/byxt34g25h</relation><date>2020-01-31T11:38:39Z</date><recordID>0.17632-byxt34g25h.1</recordID></dc>
|
| format |
Other:Dataset Other |
| author |
Santos Ferreira, Ana Sofia |
| title |
Water intensity benchmarks for sustainable retail stores |
| publisher |
Mendeley |
| publishDate |
2020 |
| topic |
Benchmarking Water Global Retailing Retail Sector |
| url |
https:/data.mendeley.com/datasets/byxt34g25h |
| contents |
The table present retrieved data of the highest revenue retailers for the research paper entitled "Water Intensity benchmarks for sustainable retail stores", according to the variables: "Water Intensity (WI)", “Company”, “Country of origin”, ”Dominant operational category”, ”Store typology”, ”Number of countries of operation”, “Retail revenue”, “Number of stores”, ”Average store sales area”, ”Total store sales area”, “Water intensity”, ”Average number of workers per store”, “Total number of workers” and “Revenue per store sales area”.
Based on this data, a WI benchmark was performed, identifying average, minimum and maximum values for each food and non-food retail sub-type, and outliers were identified with the interquartile range and removed so as to reduce error in each category. A linear regression analysis was also performed in retail sub-types that had data from three or more retailers, in order to estimate the relationship between WI as a dependent variable and other independent variables. R-squared values (R2) were calculated for each independent variable, those scoring higher than 0.7 were considered to have a strong effect size on the prediction of WI.
|
| id |
IOS7969.0.17632-byxt34g25h.1 |
| institution |
Universitas Islam Indragiri |
| affiliation |
onesearch.perpusnas.go.id |
| institution_id |
804 |
| institution_type |
library:university library |
| library |
Teknologi Pangan UNISI |
| library_id |
2816 |
| collection |
Artikel mulono |
| repository_id |
7969 |
| city |
INDRAGIRI HILIR |
| province |
RIAU |
| shared_to_ipusnas_str |
1 |
| repoId |
IOS7969 |
| first_indexed |
2020-04-08T08:18:24Z |
| last_indexed |
2020-04-08T08:18:24Z |
| recordtype |
dc |
| _version_ |
1686587538979946496 |
| score |
17.538404 |