Water intensity benchmarks for sustainable retail stores

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.

该表格展示了针对研究论文《可持续零售商店的水强度基准》所检索的最高收入零售商的数据，依据变量“水强度（WI）”、“公司”、“原产国”、“主要运营类别”、“门店类型”、“运营国家数量”、“零售收入”、“门店数量”、“平均门店销售面积”、“总门店销售面积”、“水强度”、“每家门店的平均员工数”、“总员工数”和“每平方米门店销售额”进行排列。基于此数据，对水强度基准进行了分析，识别了各类食品和非食品零售子类型的平均值、最小值和最大值，并通过四分位距识别异常值并予以剔除，以降低每个类别的误差。对于拥有三个或更多零售商数据的零售子类型，还进行了线性回归分析，以估算水强度（作为因变量）与其他自变量之间的关系。计算了每个自变量的R平方值（R²），其中得分高于0.7的变量被认为对水强度预测具有显著影响。