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28-06-2016 | LCI METHODOLOGY AND DATABASES | Issue 2/2017

The International Journal of Life Cycle Assessment 2/2017

A generalized computational structure for regional life-cycle assessment

Journal:
The International Journal of Life Cycle Assessment > Issue 2/2017
Authors:
Yi Yang, Reinout Heijungs
Important notes
Responsible editor: Rolf Frischknecht

Abstract

Purpose

Regional life-cycle assessment (LCA) is gaining an increasing attention among LCA scholars and practitioners. Here, we present a generalized computational structure for regional LCA, discuss in-depth the major challenges facing the field, and point to a direction in which we believe regional LCA should be headed.

Methods

Using an example, we first demonstrate that when there is regional heterogeneity (be it due to environmental conditions or technologies), average data would be inadequate for estimating the life-cycle impacts of a product produced in a specific region or even that of an average product produced in many regions. And when there is such regional heterogeneity, an understanding of how regions are connected through commodity flows is important to the accuracy of regional LCA estimates. Then, we present a generalized computational structure for regional LCA that takes into account interregional commodity flows, can evaluate various cases of regional differentiation, and can account for multiple impact categories simultaneously. In so doing, we show what kinds of data are required for this generalized framework of regional LCA.

Results and discussion

We discuss the major challenges facing regional LCA in terms of data requirements and computational complexity, and their implications for the choice of an optimal regional scale (i.e., the number of regions delineated within the geographic boundary studied).

Conclusions

We strongly recommend scholars from LCI and LCIA to work together and choose a spatial scale that not only adequately captures environmental characteristics but also allows inventory data to be reasonably compiled or estimated.

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