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Bridge processes: a solution for LCI datasets independent of background databases

  • COMMENTARY AND DISCUSSION ARTICLE
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Abstract

Introduction

New platforms are emerging that enable more data providers to publish life cycle inventory data.

Background

Providing datasets that are not complete LCA models results in fragments that are difficult for practitioners to integrate and use for LCA modeling. Additionally, when proxies are used to provide a technosphere input to a process that was not originally intended by the process authors, in most LCA software, this requires modifying the original process.

Results

The use of a bridge process, which is a process created to link two existing processes, is proposed as a solution.

Discussion

Benefits to bridge processes include increasing model transparency, facilitating dataset sharing and integration without compromising original dataset integrity and independence, providing a structure with which to make the data quality associated with process linkages explicit, and increasing model flexibility in the case that multiple bridges are provided. A drawback is that they add additional processes to existing LCA models which will increase their size.

Conclusions

Bridge processes can be an enabler in allowing users to integrate new datasets without modifying them to link to background databases or other processes they have available. They may not be the ideal long-term solution but provide a solution that works within the existing LCA data model.

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Acknowledgements

Colleagues in the US EPA National Risk Management Research Laboratory provided feedback and testing of bridge processes on internal LCA projects. Andreas Ciroth (GreenDelta) provided feedback on an early draft of this article.

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Correspondence to Wesley W. Ingwersen.

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This article does not reflect the endorsement or opinion of the US Environmental Protection Agency.

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Responsible editor: Andreas Ciroth

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Ingwersen, W.W., Kahn, E. & Cooper, J. Bridge processes: a solution for LCI datasets independent of background databases. Int J Life Cycle Assess 23, 2266–2270 (2018). https://doi.org/10.1007/s11367-018-1448-6

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  • DOI: https://doi.org/10.1007/s11367-018-1448-6

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