Contribution-based prioritization of LCI database improvements: the most important unit processes in ecoinvent
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 sy...
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Veröffentlicht in: | The international journal of life cycle assessment 2019-10, Vol.24 (10), p.1778-1792 |
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creator | Reinhard, Juergen Wernet, Gregor Zah, Rainer Heijungs, Reinout Hilty, Lorenz M. |
description | 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. |
doi_str_mv | 10.1007/s11367-019-01602-0 |
format | Article |
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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.</description><identifier>ISSN: 0948-3349</identifier><identifier>EISSN: 1614-7502</identifier><identifier>DOI: 10.1007/s11367-019-01602-0</identifier><language>eng</language><publisher>Berlin/Heidelberg: Springer Berlin Heidelberg</publisher><subject>Algorithms ; Climate change ; Data Availability ; Data Quality ; Datasets ; Earth and Environmental Science ; Environment ; Environmental Chemistry ; Environmental Economics ; Environmental Engineering/Biotechnology ; Environmental impact ; Indicators ; Intersections ; Life cycle assessment ; Life cycles ; Meta-analysis ; Quality ; Ranking ; Waste treatment</subject><ispartof>The international journal of life cycle assessment, 2019-10, Vol.24 (10), p.1778-1792</ispartof><rights>Springer-Verlag GmbH Germany, part of Springer Nature 2019</rights><rights>The International Journal of Life Cycle Assessment is a copyright of Springer, (2019). All Rights Reserved.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c421t-ca1c9ea5d457466eb14d3549ac2ea8f2158f2dd22ea185b011d3ee884d7183503</citedby><cites>FETCH-LOGICAL-c421t-ca1c9ea5d457466eb14d3549ac2ea8f2158f2dd22ea185b011d3ee884d7183503</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s11367-019-01602-0$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s11367-019-01602-0$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>314,780,784,27923,27924,41487,42556,51318</link.rule.ids></links><search><creatorcontrib>Reinhard, Juergen</creatorcontrib><creatorcontrib>Wernet, Gregor</creatorcontrib><creatorcontrib>Zah, Rainer</creatorcontrib><creatorcontrib>Heijungs, Reinout</creatorcontrib><creatorcontrib>Hilty, Lorenz M.</creatorcontrib><title>Contribution-based prioritization of LCI database improvements: the most important unit processes in ecoinvent</title><title>The international journal of life cycle assessment</title><addtitle>Int J Life Cycle Assess</addtitle><description>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.</description><subject>Algorithms</subject><subject>Climate change</subject><subject>Data Availability</subject><subject>Data Quality</subject><subject>Datasets</subject><subject>Earth and Environmental Science</subject><subject>Environment</subject><subject>Environmental Chemistry</subject><subject>Environmental Economics</subject><subject>Environmental Engineering/Biotechnology</subject><subject>Environmental impact</subject><subject>Indicators</subject><subject>Intersections</subject><subject>Life cycle assessment</subject><subject>Life cycles</subject><subject>Meta-analysis</subject><subject>Quality</subject><subject>Ranking</subject><subject>Waste treatment</subject><issn>0948-3349</issn><issn>1614-7502</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2019</creationdate><recordtype>article</recordtype><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><recordid>eNp9kEtPxCAUhYnRxPHxB1yRuK5yKbTUnWl8TDKJG10TWqgysTACnUR_vdSauHPBJVzOOVw-hC6AXAEh9XUEKKu6INDkVRFakAO0ggpYUXNCD9GKNEwUZcmaY3QS45YQCqThK-Ra71Kw3ZSsd0WnotF4F6wPNtkvNTexH_CmXWOtkprvsR13we_NaFyKNzi9GTz6mOa2D0m5hCdnUw7xvYnRRGwdNr23bp8NZ-hoUO_RnP_up-jl_u65fSw2Tw_r9nZT9IxCKnoFfWMU14zXrKpMB0yXnDWqp0aJgQLPRWuaTyB4RwB0aYwQTNcgSk7KU3S55OYxPiYTk9z6Kbj8pKQghMiMmllFF1UffIzBDDJ_fVThUwKRM1e5cJWZq_zhKmdTuZhiFrtXE_6i_3F9A8YBfPs</recordid><startdate>20191001</startdate><enddate>20191001</enddate><creator>Reinhard, Juergen</creator><creator>Wernet, Gregor</creator><creator>Zah, Rainer</creator><creator>Heijungs, Reinout</creator><creator>Hilty, Lorenz M.</creator><general>Springer Berlin Heidelberg</general><general>Springer Nature B.V</general><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7ST</scope><scope>7TB</scope><scope>7XB</scope><scope>88I</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>8FK</scope><scope>ABJCF</scope><scope>ABUWG</scope><scope>AEUYN</scope><scope>AFKRA</scope><scope>ATCPS</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>BHPHI</scope><scope>C1K</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>F28</scope><scope>FR3</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>KR7</scope><scope>L6V</scope><scope>M2P</scope><scope>M7S</scope><scope>PATMY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PTHSS</scope><scope>PYCSY</scope><scope>Q9U</scope><scope>SOI</scope></search><sort><creationdate>20191001</creationdate><title>Contribution-based prioritization of LCI database improvements: the most important unit processes in ecoinvent</title><author>Reinhard, Juergen ; Wernet, Gregor ; Zah, Rainer ; Heijungs, Reinout ; Hilty, Lorenz M.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c421t-ca1c9ea5d457466eb14d3549ac2ea8f2158f2dd22ea185b011d3ee884d7183503</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2019</creationdate><topic>Algorithms</topic><topic>Climate change</topic><topic>Data Availability</topic><topic>Data Quality</topic><topic>Datasets</topic><topic>Earth and Environmental Science</topic><topic>Environment</topic><topic>Environmental Chemistry</topic><topic>Environmental Economics</topic><topic>Environmental Engineering/Biotechnology</topic><topic>Environmental impact</topic><topic>Indicators</topic><topic>Intersections</topic><topic>Life cycle assessment</topic><topic>Life cycles</topic><topic>Meta-analysis</topic><topic>Quality</topic><topic>Ranking</topic><topic>Waste treatment</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Reinhard, Juergen</creatorcontrib><creatorcontrib>Wernet, Gregor</creatorcontrib><creatorcontrib>Zah, Rainer</creatorcontrib><creatorcontrib>Heijungs, Reinout</creatorcontrib><creatorcontrib>Hilty, Lorenz M.</creatorcontrib><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Environment Abstracts</collection><collection>Mechanical & Transportation Engineering Abstracts</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Science Database (Alumni Edition)</collection><collection>Technology Research Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>Materials Science & Engineering Collection</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest One Sustainability</collection><collection>ProQuest Central UK/Ireland</collection><collection>Agricultural & Environmental Science Collection</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>Technology Collection (ProQuest)</collection><collection>Natural Science Collection (ProQuest)</collection><collection>Environmental Sciences and Pollution Management</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>ANTE: Abstracts in New Technology & Engineering</collection><collection>Engineering Research Database</collection><collection>ProQuest Central Student</collection><collection>SciTech Premium Collection</collection><collection>Civil Engineering Abstracts</collection><collection>ProQuest Engineering Collection</collection><collection>Science Database</collection><collection>Engineering Database</collection><collection>Environmental Science Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>Engineering Collection</collection><collection>Environmental Science Collection</collection><collection>ProQuest Central Basic</collection><collection>Environment Abstracts</collection><jtitle>The international journal of life cycle assessment</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Reinhard, Juergen</au><au>Wernet, Gregor</au><au>Zah, Rainer</au><au>Heijungs, Reinout</au><au>Hilty, Lorenz M.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Contribution-based prioritization of LCI database improvements: the most important unit processes in ecoinvent</atitle><jtitle>The international journal of life cycle assessment</jtitle><stitle>Int J Life Cycle Assess</stitle><date>2019-10-01</date><risdate>2019</risdate><volume>24</volume><issue>10</issue><spage>1778</spage><epage>1792</epage><pages>1778-1792</pages><issn>0948-3349</issn><eissn>1614-7502</eissn><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.</abstract><cop>Berlin/Heidelberg</cop><pub>Springer Berlin Heidelberg</pub><doi>10.1007/s11367-019-01602-0</doi><tpages>15</tpages><oa>free_for_read</oa></addata></record> |
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subjects | Algorithms Climate change Data Availability Data Quality Datasets Earth and Environmental Science Environment Environmental Chemistry Environmental Economics Environmental Engineering/Biotechnology Environmental impact Indicators Intersections Life cycle assessment Life cycles Meta-analysis Quality Ranking Waste treatment |
title | Contribution-based prioritization of LCI database improvements: the most important unit processes in ecoinvent |
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