A Structure Comparison of two Approaches to LCA Inventory Data, Based on the MIET and ETH Databases

Mongelli I, Suh S, Huppes G (2005)

Publication info

International Journal of Life Cycle Assessment 10(5):317-324


Goal and Scope

This study compared two different approaches to general inventory data in LCA, one involving the process-based ETH 96 database and the other an environmentally extended Input-Output table for the US, referring to MIET (Missing Inventory Estimation Tool) 2.0. The purpose of the present paper is to highlight and explain some of the differences between the two approaches, in order to give LCA practitioners a clearer idea of the advantages and limitations of using Input-Output analysis combined with process LCA.


The comparison was made despite substantial differences between the two approaches, through a reduction and reclassification of the ETH process technology matrix to fit the Input-Output classification scheme and by concentrating on the structure of the processes rather than their absolute values. The structure is described in terms of the percentage of the CO2 contribution to the total emission by all processes involved in the supply chain. An input and output structure comparison was carried out between ETH 96 and MIET 2.0, to extract information about their structures.

Results and Discussion

The results of the study show that, despite their methodological differences, MIET 2.0 and ETH 96 show substantial similarities in their overall structures. There are also differences in the structure of the two databases, and most of them have occurred randomly, while, for certain particular sectors, the differences are rather persistent. Especially the contributions by capital goods are constantly lower in ETH 96 database and vice versa. The results imply possible systematic truncation in process LCA databases, especially for a few sectors such as capital goods.

Recommendation and Perspective

Hybrid analysis can overcome the problem of incompleteness in process LCA, while avoiding such disadvantages of IOA as aggregation problem.

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