Contribution-based prioritization of LCI database improvements: the most important unit processes in ecoinvent

Juergen Reinhard, Gregor Wernet, Rainer Zah, Reinout Heijungs, Lorenz M. Hilty

Research output: Contribution to JournalArticleAcademicpeer-review

Abstract

Purpose: Improving the quality and quantity of unit process datasets in Life Cycle Inventory (LCI) databases affects every LCA they are used in. However, improvements in data quality and quantity are so far rather directed by the external supply of data and situation-driven requirements instead of systematic choices guided by structural dependencies in the data. Overall, the impact of current data updates on the quality of the LCI database remains unclear and maintenance efforts might be ineffective. This article analyzes how a contribution-based prioritization approach can direct LCI update efforts to datasets of key importance. Methods: A contribution-based prioritization method has been applied to version 3 of the ecoinvent database. We identified the relevance of unit processes on the basis of their relative contributions throughout each product system with respect to a broad range of Life Cycle Impact Assessment (LCIA) indicators. A novel ranking algorithm enabled the ranking of unit processes according to their impact on the LCIA results. Finally, we identified the most relevant unit processes for different sectors and geographies. Results and discussion: The study shows that a relatively large proportion of the overall database quality is dependent on a small set of key processes. Processes related to electricity generation, waste treatment activities, and energy carrier provision (petroleum and hard coal) consistently cause large environmental impacts on all product systems. Overall, 300 datasets are causing 60% of the environmental impacts across all LCIA indicators, while only 3 datasets are causing 11% of all climate change impacts. In addition, our analysis highlights the presence and importance of central hubs, i.e., sensitive intersections in the database network, whose modification can affect a large proportion of database quality. Conclusions: Our study suggests that contribution-based prioritization offers important insights into the systematic and effective improvement of LCI databases. The presented list of key processes in ecoinvent version 3.1 adds a new perspective to database improvements as it allows the allocation of available resources according to the structural dependencies in the data.

Original languageEnglish
JournalInternational Journal of Life Cycle Assessment
DOIs
Publication statusPublished - 1 Jan 2019

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prioritization
life cycle
ranking
environmental impact
electricity generation
waste treatment
data quality
petroleum
coal
climate change
resource

Keywords

  • Contribution analysis
  • Life Cycle Assessment
  • Life Cycle Inventory database management
  • Meta-analysis
  • Prioritization

Cite this

@article{4464971625f3467eae41f4e6fc8de6bc,
title = "Contribution-based prioritization of LCI database improvements: the most important unit processes in ecoinvent",
abstract = "Purpose: Improving the quality and quantity of unit process datasets in Life Cycle Inventory (LCI) databases affects every LCA they are used in. However, improvements in data quality and quantity are so far rather directed by the external supply of data and situation-driven requirements instead of systematic choices guided by structural dependencies in the data. Overall, the impact of current data updates on the quality of the LCI database remains unclear and maintenance efforts might be ineffective. This article analyzes how a contribution-based prioritization approach can direct LCI update efforts to datasets of key importance. Methods: A contribution-based prioritization method has been applied to version 3 of the ecoinvent database. We identified the relevance of unit processes on the basis of their relative contributions throughout each product system with respect to a broad range of Life Cycle Impact Assessment (LCIA) indicators. A novel ranking algorithm enabled the ranking of unit processes according to their impact on the LCIA results. Finally, we identified the most relevant unit processes for different sectors and geographies. Results and discussion: The study shows that a relatively large proportion of the overall database quality is dependent on a small set of key processes. Processes related to electricity generation, waste treatment activities, and energy carrier provision (petroleum and hard coal) consistently cause large environmental impacts on all product systems. Overall, 300 datasets are causing 60{\%} of the environmental impacts across all LCIA indicators, while only 3 datasets are causing 11{\%} of all climate change impacts. In addition, our analysis highlights the presence and importance of central hubs, i.e., sensitive intersections in the database network, whose modification can affect a large proportion of database quality. Conclusions: Our study suggests that contribution-based prioritization offers important insights into the systematic and effective improvement of LCI databases. The presented list of key processes in ecoinvent version 3.1 adds a new perspective to database improvements as it allows the allocation of available resources according to the structural dependencies in the data.",
keywords = "Contribution analysis, Life Cycle Assessment, Life Cycle Inventory database management, Meta-analysis, Prioritization",
author = "Juergen Reinhard and Gregor Wernet and Rainer Zah and Reinout Heijungs and Hilty, {Lorenz M.}",
year = "2019",
month = "1",
day = "1",
doi = "10.1007/s11367-019-01602-0",
language = "English",
journal = "International Journal of Life Cycle Assessment",
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Contribution-based prioritization of LCI database improvements : the most important unit processes in ecoinvent. / Reinhard, Juergen; Wernet, Gregor; Zah, Rainer; Heijungs, Reinout; Hilty, Lorenz M.

In: International Journal of Life Cycle Assessment, 01.01.2019.

Research output: Contribution to JournalArticleAcademicpeer-review

TY - JOUR

T1 - Contribution-based prioritization of LCI database improvements

T2 - the most important unit processes in ecoinvent

AU - Reinhard, Juergen

AU - Wernet, Gregor

AU - Zah, Rainer

AU - Heijungs, Reinout

AU - Hilty, Lorenz M.

PY - 2019/1/1

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N2 - Purpose: Improving the quality and quantity of unit process datasets in Life Cycle Inventory (LCI) databases affects every LCA they are used in. However, improvements in data quality and quantity are so far rather directed by the external supply of data and situation-driven requirements instead of systematic choices guided by structural dependencies in the data. Overall, the impact of current data updates on the quality of the LCI database remains unclear and maintenance efforts might be ineffective. This article analyzes how a contribution-based prioritization approach can direct LCI update efforts to datasets of key importance. Methods: A contribution-based prioritization method has been applied to version 3 of the ecoinvent database. We identified the relevance of unit processes on the basis of their relative contributions throughout each product system with respect to a broad range of Life Cycle Impact Assessment (LCIA) indicators. A novel ranking algorithm enabled the ranking of unit processes according to their impact on the LCIA results. Finally, we identified the most relevant unit processes for different sectors and geographies. Results and discussion: The study shows that a relatively large proportion of the overall database quality is dependent on a small set of key processes. Processes related to electricity generation, waste treatment activities, and energy carrier provision (petroleum and hard coal) consistently cause large environmental impacts on all product systems. Overall, 300 datasets are causing 60% of the environmental impacts across all LCIA indicators, while only 3 datasets are causing 11% of all climate change impacts. In addition, our analysis highlights the presence and importance of central hubs, i.e., sensitive intersections in the database network, whose modification can affect a large proportion of database quality. Conclusions: Our study suggests that contribution-based prioritization offers important insights into the systematic and effective improvement of LCI databases. The presented list of key processes in ecoinvent version 3.1 adds a new perspective to database improvements as it allows the allocation of available resources according to the structural dependencies in the data.

AB - Purpose: Improving the quality and quantity of unit process datasets in Life Cycle Inventory (LCI) databases affects every LCA they are used in. However, improvements in data quality and quantity are so far rather directed by the external supply of data and situation-driven requirements instead of systematic choices guided by structural dependencies in the data. Overall, the impact of current data updates on the quality of the LCI database remains unclear and maintenance efforts might be ineffective. This article analyzes how a contribution-based prioritization approach can direct LCI update efforts to datasets of key importance. Methods: A contribution-based prioritization method has been applied to version 3 of the ecoinvent database. We identified the relevance of unit processes on the basis of their relative contributions throughout each product system with respect to a broad range of Life Cycle Impact Assessment (LCIA) indicators. A novel ranking algorithm enabled the ranking of unit processes according to their impact on the LCIA results. Finally, we identified the most relevant unit processes for different sectors and geographies. Results and discussion: The study shows that a relatively large proportion of the overall database quality is dependent on a small set of key processes. Processes related to electricity generation, waste treatment activities, and energy carrier provision (petroleum and hard coal) consistently cause large environmental impacts on all product systems. Overall, 300 datasets are causing 60% of the environmental impacts across all LCIA indicators, while only 3 datasets are causing 11% of all climate change impacts. In addition, our analysis highlights the presence and importance of central hubs, i.e., sensitive intersections in the database network, whose modification can affect a large proportion of database quality. Conclusions: Our study suggests that contribution-based prioritization offers important insights into the systematic and effective improvement of LCI databases. The presented list of key processes in ecoinvent version 3.1 adds a new perspective to database improvements as it allows the allocation of available resources according to the structural dependencies in the data.

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