Research

Use of data envelopment analysis to benchmark environmental product declarations—a suggested framework

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Abstract

Purpose: Environmental product declarations (EPDs) are standardized tools based on life cycle assessment (LCA) to communicate and compare environmental performance of products according to well-defined product category rules (PCRs). However, despite the comparability enabled by the PCRs, the use of this information for benchmarking is still challenging, since there is still no consensus or standardization regarding techniques and procedures to be adopted. Therefore, here, we suggest and apply a framework to benchmark and develop a ranking system for the products based on data from EPDs. Methods: The proposed framework is based on efficiency assessment using data envelopment analysis (DEA). The main advantages of DEA are that it does not require potentially non-scientific factors, e.g., for normalization and that it provides an efficiency score based on all the used indicators, rather than just based on a subset of them. A five-step benchmarking framework is presented which includes data collection from EPDs, statistical analyses, selection of the variables, application of DEA, and cluster analysis to establish the environmental performance ranking on a scale from A (best) to E (worst). In order to illustrate the applicability of the proposal, two case studies of different product categories are presented: bakery products and insulation materials. Results and discussion: In the first case, 72 bakery products are evaluated of which 9 were ranked in the category A, while 9 other products are considered the most inefficient and were ranked in category E. For insulation materials, 89 products are evaluated and 9 are categorized as A of which 5 are considered efficient, while the category E comprises 20 products. The results obtained through DEA are compared with those from other approaches (being internal normalization and external normalization, respectively, each with subsequent aggregation) to obtain single scores. There are significant differences in the results obtained from the three approaches, especially when dealing with trade-offs, because DEA does not relate the scores to specific values, but to a so-called “efficiency frontier”. Conclusions: The use of DEA allows an analysis based on the efficiency of the products in terms of environmental performance and can be applied using data from EPDs. The framework proposed allows benchmarking of the results and opens a new perspective to contribute to the issue of limitations in the communication function of EPDs. However, DEA is limited to analyzing the efficiency of a given product in relation to a pre-determined group.