Benchmarking green logistics performance with a composite index

Purpose – This paper aims to discuss the development and use of a green logistics performance index (GLPI) for easy comparison of performance among industries and countries. It uses the survey data collected from the home electronic appliance industry in China and Japan as an example to demonstrate the index development process and compare the performance of green logistics (GL) practices between the two countries using the proposed index. Design/methodology/approach – Two‐sample t ‐test and one‐way analysis of variance (ANOVA) were used to analyse the data collected from a questionnaire survey. Principal component analysis (PCA) was employed to derive the weights from the survey data for the GLPI. Findings – The findings reveal that the GLPI derived using PCA is robust and gives similar results as obtained through two‐sample t ‐test and ANOVA of the dataset in the comparison of performance among firms and between countries in the study. Research limitations/implications – This study lends insight into the use of an objectively derived composite index to measure and compare GL performance. To serve mainly as a proof of concept and to enhance response rate in the questionnaire survey, the scope of the study is limited to three major logistics functions in an industry in two countries. Practical implications – Managers can use the GLPI to benchmark their performance in the respective logistics areas and revise their supply chain strategy accordingly. The proposed index may also assist governments in formulating policies on promoting their GL implementation. Social implications – A comprehensive composite index to benchmark GL performance can facilitate and encourage industries to invest in GL. This will help reduce negative impacts of logistics activities on the environment. Originality/value – Research in GL to date has largely focused on theory and management approach. This paper fills the gap in the literature by empirically comparing GL performance among firms and countries through the use of a composite index. It also contributes to a better understanding of the association between GL performance and firm size as well as the driving factors behind it.