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Christian Bauer - One of the best experts on this subject based on the ideXlab platform.
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Should we neglect cement carbonation in life cycle inventory Databases
International Journal of Life Cycle Assessment, 2020Co-Authors: Romain Sacchi, Christian BauerAbstract:This study assesses the effect of including CO2 uptake by cement-containing materials (CCM) in background life cycle inventories, on comparative life cycle assessments. The carbonation of CCM consumed by transforming activities in the Ecoinvent Database is estimated. The uncertainties around parameters that affect cement carbonation (e.g., geometry and environment types) are approximated by error propagation. Five pairwise comparisons of functionally equivalent product systems are conducted in two parallel Monte Carlo simulations to isolate the effect of cement carbonation using pre-sampled values. Based on the five comparative assessments, there is a maximum probability of 3.7% that including cement carbonation in background inventories of Ecoinvent can affect end results to the extent of changing conclusions. While a probability of 3.7% is admittedly low, this finding is exclusively based on Ecoinvent inventories. Therefore, should the inventories rely on background activities that consume CCM to a larger extent, or from another Database, this probability may be higher. It is difficult to state whether including cement carbonation in background inventories is likely to significantly change the outcome of a comparative study. It seems though that neglecting cement carbonation in background inventories currently creates, at best, a mild bias in favor of CCM-poor product systems. Given the clear and easy-to-implement method presented in this study, the authors recommend including cement carbonation in the future development of life cycle inventory Databases.
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Should we neglect cement carbonation in life cycle inventory Databases?
The International Journal of Life Cycle Assessment, 2020Co-Authors: Romain Sacchi, Christian BauerAbstract:Purpose This study assesses the effect of including CO_2 uptake by cement-containing materials (CCM) in background life cycle inventories, on comparative life cycle assessments. Methods The carbonation of CCM consumed by transforming activities in the Ecoinvent Database is estimated. The uncertainties around parameters that affect cement carbonation (e.g., geometry and environment types) are approximated by error propagation. Five pairwise comparisons of functionally equivalent product systems are conducted in two parallel Monte Carlo simulations to isolate the effect of cement carbonation using pre-sampled values. Results and discussion Based on the five comparative assessments, there is a maximum probability of 3.7% that including cement carbonation in background inventories of Ecoinvent can affect end results to the extent of changing conclusions. While a probability of 3.7% is admittedly low, this finding is exclusively based on Ecoinvent inventories. Therefore, should the inventories rely on background activities that consume CCM to a larger extent, or from another Database, this probability may be higher. Conclusions It is difficult to state whether including cement carbonation in background inventories is likely to significantly change the outcome of a comparative study. It seems though that neglecting cement carbonation in background inventories currently creates, at best, a mild bias in favor of CCM-poor product systems. Given the clear and easy-to-implement method presented in this study, the authors recommend including cement carbonation in the future development of life cycle inventory Databases.
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Life cycle inventories of electricity generation and power supply in version 3 of the Ecoinvent Database—part I: electricity generation
The International Journal of Life Cycle Assessment, 2016Co-Authors: Karin Treyer, Christian BauerAbstract:PurposeLife cycle inventories (LCI) of electricity generation and supply are among the main determining factors regarding life cycle assessment (LCA) results. Therefore, consistency and representativeness of these data are crucial. The electricity sector has been updated and substantially extended for Ecoinvent version 3 (v3). This article provides an overview of the electricity production datasets and insights into key aspects of these v3 inventories, highlights changes and describes new features.MethodsMethods involved extraction of data and analysis from several publically accessible Databases and statistics, as well as from the LCA literature. Depending on the power generation technology, either plant-specific or region-specific average data have been used for creating the new power generation inventories representing specific geographies. Whenever possible, the parent–child relationship was used between global and local activities. All datasets include a specific technology level in order to support marginal mixes used in the consequential version of Ecoinvent. The use of parameters, variables and mathematical relations enhances transparency. The article focuses on documentation of LCI data on the unlinked unit process level and presents direct emission data of the electricity-generating activities.Results and discussionDatasets for electricity production in 71 geographic regions (geographies) covering 50 countries are available in Ecoinvent v3. The number of geographies exceeds the number of countries due to partitioning of power generation in the USA and Canada into several regions. All important technologies representing fossil, renewable and nuclear power are modelled for all geographies. The new inventory data show significant geography-specific variations: thermal power plant efficiencies, direct air pollutant emissions as well as annual yields of photovoltaic and wind power plants will have significant impacts on cumulative inventories. In general, the power plants operating in the 18 newly implemented countries (compared to Ecoinvent v2) are on a lower technology level with lower efficiencies and higher emissions. The importance of local datasets is once more highlighted.ConclusionsInventories for average technology-specific electricity production in all globally important economies are now available with geography-specific technology datasets. This improved coverage of power generation representing 83 % of global electricity production in 2008 will increase the quality of and reduce uncertainties in LCA studies worldwide and contribute to a more accurate estimation of environmental burdens from global production chains. Future work on LCI of electricity production should focus on updates of the fuel chain and infrastructure datasets, on including new technologies as well as on refining of the local data.
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life cycle inventories of electricity generation and power supply in version 3 of the Ecoinvent Database part i electricity generation
International Journal of Life Cycle Assessment, 2016Co-Authors: Karin Treyer, Christian BauerAbstract:Purpose Life cycle inventories (LCI) of electricity generation and supply are among the main determining factors regarding life cycle assessment (LCA) results. Therefore, consistency and representativeness of these data are crucial. The electricity sector has been updated and substantially extended for Ecoinvent version 3 (v3). This article provides an overview of the electricity production datasets and insights into key aspects of these v3 inventories, highlights changes and describes new features.
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The Ecoinvent Database version 3 (part I): overview and methodology
The International Journal of Life Cycle Assessment, 2016Co-Authors: Gregor Wernet, Emilia Moreno-ruiz, Jürgen Reinhard, Bernhard Steubing, Christian Bauer, Bo Pedersen WeidemaAbstract:Purpose Good background data are an important requirement in LCA. Practitioners generally make use of LCI Databases for such data, and the Ecoinvent Database is the largest transparent unit-process LCI Database worldwide. Since its first release in 2003, it has been continuously updated, and version 3 was published in 2013. The release of version 3 introduced several significant methodological and technological improvements, besides a large number of new and updated datasets. The aim was to expand the content of the Database, set the foundation for a truly global Database, support regionalized LCIA, offer multiple system models, allow for easier integration of data from different regions, and reduce maintenance efforts. This article describes the methodological developments. Methods Modeling choices and raw data were separated in version 3, which enables the application of different sets of modeling choices, or system models, to the same raw data with little effort. This includes one system model for Consequential LCA. Flow properties were added to all exchanges in the Database, giving more information on the inventory and allowing a fast calculation of mass and other balances. With version 3.1, the Database is generally water-balanced, and water use and consumption can be determined. Consumption mixes called market datasets were consistently added to the Database, and global background data was added, often as an extrapolation from regional data. Results and discussion In combination with hundreds of new unit processes from regions outside Europe, these changes lead to an improved modeling of global supply chains, and a more realistic distribution of impacts in regionalized LCIA. The new mixes also facilitate further regionalization due to the availability of background data for all regions. Conclusions With version 3, the Ecoinvent Database substantially expands the goals and scopes of LCA studies it can support. The new system models allow new, different studies to be performed. Global supply chains and market datasets significantly increase the relevance of the Database outside of Europe, and regionalized LCA is supported by the data. Datasets are more transparent, include more information, and support, e.g., water balances. The developments also support easier collaboration with other Database initiatives, as demonstrated by a first successful collaboration with a data project in Québec. Version 3 has set the foundation for expanding Ecoinvent from a mostly regional into a truly global Database and offers many new insights beyond the thousands of new and updated datasets it also introduced.
Emilia Morenoruiz - One of the best experts on this subject based on the ideXlab platform.
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first series of seafood datasets in Ecoinvent setting the pace for future development
International Journal of Life Cycle Assessment, 2020Co-Authors: Angel Avadi, Avraam Symeonidis, Ia Vazquezrowe, Emilia MorenoruizAbstract:Purpose The number of life cycle assessment studies related to seafood has risen considerably in the past decade. Despite this proliferation, major life cycle inventory Databases tend to lack information describing this sector. Hence, the main objectives of this study are to present the first effort to aggregate and standardize seafood-related datasets in the Ecoinvent Database and to explain the main data sources and methodological choices used in the building of the datasets.
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integrating regionalized brazilian land use change datasets into the Ecoinvent Database new data premises and uncertainties have large effects in the results
International Journal of Life Cycle Assessment, 2020Co-Authors: Ana Cristina Guimarães Donke, Renan Milagres Lage Novaes, Ricardo Antonio Almeida Pazianotto, Juliana Ferreira Picoli, Emilia Morenoruiz, Jurge Reinhard, Marilia Ieda Da Silveira FolegattimatsuuraAbstract:Land use change (LUC) is a critical process in the life cycle greenhouse gas emissions of agricultural products and Brazil is a major exporter of these. This work had the objective of integrating refined and regionalized datasets of LUC in Brazil into the Ecoinvent Database, to better represent its dynamics and heterogeneity. We present the adaptations needs for having it suitable for crops, pasture and forestry in state-level and impacts of modelling assumptions and uncertainties. Adaptation and integration were based in Ecoinvent version 3 guidelines and the Database requirements to LUC modelling. BRLUC, a method for Brazilian LUC accounting, was the main data source. The workflow for the integration process consisted in identifying necessary adaptations in both sources to allow a better representation of Brazilian LUC. Four new reference products and 27 geographies were added in the Database. A total of 566 new datasets were integrated into Ecoinvent version 3.6, allowing the incorporation of LUC in Brazilian products in state, regional and national level. GHG emissions reduced, being 42.2% and 99.9% lower to soybean and sugarcane than in Ecoinvent v3.5. Four improvements were the main causes: (i) state-level LUC modelling with national official data; (ii) regionalizing carbon stocks; (iii) including pasture and forestry land use categories; (iv) and considering sugarcane as a perennial crop. The way to calculate national-level results based on subnational data was an important source of difference in emissions too. Uncertainties specifically associated with land use substitution patterns were not incorporated, and they can potentially have impacts as large as the uncertainties of all the remaining processes combined. Results showed that small changes in data sources and premises have large impacts on emissions associated with LUC in agricultural products. It also showed the large impacts of uncertainties of LUC patterns. Improving current models in better representing regional LUC patterns, regional carbon stocks and uncertainty accounting could reduce these impacts. Nonetheless, efforts in reducing the complexity of LUC accounting methods could enhance transparency and effectiveness.
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the Ecoinvent Database version 3 part i overview and methodology
International Journal of Life Cycle Assessment, 2016Co-Authors: Gregor Wernet, Jürgen Reinhard, Emilia Morenoruiz, Bernhard Steubing, Christian Bauer, Bo Pedersen WeidemaAbstract:Purpose Good background data are an important requirement in LCA. Practitioners generally make use of LCI Databases for such data, and the Ecoinvent Database is the largest transparent unit-process LCI Database worldwide. Since its first release in 2003, it has been continuously updated, and version 3 was published in 2013. The release of version 3 introduced several significant methodological and technological improvements, besides a large number of new and updated datasets. The aim was to expand the content of the Database, set the foundation for a truly global Database, support regionalized LCIA, offer multiple system models, allow for easier integration of data from different regions, and reduce maintenance efforts. This article describes the methodological developments.
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the Ecoinvent Database version 3 part ii analyzing lca results and comparison to version 2
International Journal of Life Cycle Assessment, 2016Co-Authors: Ernhard Steubing, Jurge Reinhard, Grego Werne, Christia Aue, Emilia MorenoruizAbstract:Purpose Version 3 of Ecoinvent includes more data, new modeling principles, and, for the first time, several system models: the “Allocation, cut-off by classification” (Cut-off) system model, which replicates the modeling principles of version 2, and two newly introduced models called “Allocation at the point of substitution” (APOS) and “Consequential” (Wernet et al. 2016). The aim of this paper is to analyze and explain the differences in life cycle impact assessment (LCIA) results of the v3.1 Cut-off system model in comparison to v2.2 as well as the APOS and Consequential system models.
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the Ecoinvent Database version 3 part ii analyzing lca results and comparison to version 2
International Journal of Life Cycle Assessment, 2016Co-Authors: Jürgen Reinhard, Gregor Wernet, Bernhard Steubing, Christian Bauer, Emilia MorenoruizAbstract:Version 3 of Ecoinvent includes more data, new modeling principles, and, for the first time, several system models: the “Allocation, cut-off by classification” (Cut-off) system model, which replicates the modeling principles of version 2, and two newly introduced models called “Allocation at the point of substitution” (APOS) and “Consequential” (Wernet et al. 2016). The aim of this paper is to analyze and explain the differences in life cycle impact assessment (LCIA) results of the v3.1 Cut-off system model in comparison to v2.2 as well as the APOS and Consequential system models. In order to do this, functionally equivalent datasets were matched across Database versions and LCIA results compared to each other. In addition, the contribution of specific sectors was analyzed. The importance of new and updated data as well as new modeling principles is illustrated through examples. Differences were observed in between all Database versions using the impact assessment methods Global Warming Potential (GWP100a), ReCiPe Endpoint (H/A), and Ecological Scarcity 2006 (ES’06). The highest differences were found for the comparison of the v3.1 Cut-off and v2.2. At average, LCIA results increased by 6, 8, and 17 % and showed a median dataset deviation of 13, 13, and 21 % for GWP, ReCiPe, and ES’06, respectively. These changes are due to the simultaneous update and addition of new data as well as through the introduction of global coverage and spatially consistent linking of activities throughout the Database. As a consequence, supply chains are now globally better represented than in version 2 and lead, e.g., in the electricity sector, to more realistic life cycle inventory (LCI) background data. LCIA results of the Cut-off and APOS models are similar and differ mainly for recycling materials and wastes. In contrast, LCIA results of the Consequential version differ notably from the attributional system models, which is to be expected due to fundamentally different modeling principles. The use of marginal instead of average suppliers in markets, i.e., consumption mixes, is the main driver for result differences. LCIA results continue to change as LCI Databases evolve, which is confirmed by a historical comparison of v1.3 and v2.2. Version 3 features more up-to-date background data as well as global supply chains and should, therefore, be used instead of previous versions. Continuous efforts will be required to decrease the contribution of Rest-of-the-World (RoW) productions and thereby improve the global coverage of supply chains.
Bernhard Steubing - One of the best experts on this subject based on the ideXlab platform.
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The Ecoinvent Database version 3 (part I): overview and methodology
The International Journal of Life Cycle Assessment, 2016Co-Authors: Gregor Wernet, Emilia Moreno-ruiz, Jürgen Reinhard, Bernhard Steubing, Christian Bauer, Bo Pedersen WeidemaAbstract:Purpose Good background data are an important requirement in LCA. Practitioners generally make use of LCI Databases for such data, and the Ecoinvent Database is the largest transparent unit-process LCI Database worldwide. Since its first release in 2003, it has been continuously updated, and version 3 was published in 2013. The release of version 3 introduced several significant methodological and technological improvements, besides a large number of new and updated datasets. The aim was to expand the content of the Database, set the foundation for a truly global Database, support regionalized LCIA, offer multiple system models, allow for easier integration of data from different regions, and reduce maintenance efforts. This article describes the methodological developments. Methods Modeling choices and raw data were separated in version 3, which enables the application of different sets of modeling choices, or system models, to the same raw data with little effort. This includes one system model for Consequential LCA. Flow properties were added to all exchanges in the Database, giving more information on the inventory and allowing a fast calculation of mass and other balances. With version 3.1, the Database is generally water-balanced, and water use and consumption can be determined. Consumption mixes called market datasets were consistently added to the Database, and global background data was added, often as an extrapolation from regional data. Results and discussion In combination with hundreds of new unit processes from regions outside Europe, these changes lead to an improved modeling of global supply chains, and a more realistic distribution of impacts in regionalized LCIA. The new mixes also facilitate further regionalization due to the availability of background data for all regions. Conclusions With version 3, the Ecoinvent Database substantially expands the goals and scopes of LCA studies it can support. The new system models allow new, different studies to be performed. Global supply chains and market datasets significantly increase the relevance of the Database outside of Europe, and regionalized LCA is supported by the data. Datasets are more transparent, include more information, and support, e.g., water balances. The developments also support easier collaboration with other Database initiatives, as demonstrated by a first successful collaboration with a data project in Québec. Version 3 has set the foundation for expanding Ecoinvent from a mostly regional into a truly global Database and offers many new insights beyond the thousands of new and updated datasets it also introduced.
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the Ecoinvent Database version 3 part i overview and methodology
International Journal of Life Cycle Assessment, 2016Co-Authors: Gregor Wernet, Jürgen Reinhard, Emilia Morenoruiz, Bernhard Steubing, Christian Bauer, Bo Pedersen WeidemaAbstract:Purpose Good background data are an important requirement in LCA. Practitioners generally make use of LCI Databases for such data, and the Ecoinvent Database is the largest transparent unit-process LCI Database worldwide. Since its first release in 2003, it has been continuously updated, and version 3 was published in 2013. The release of version 3 introduced several significant methodological and technological improvements, besides a large number of new and updated datasets. The aim was to expand the content of the Database, set the foundation for a truly global Database, support regionalized LCIA, offer multiple system models, allow for easier integration of data from different regions, and reduce maintenance efforts. This article describes the methodological developments.
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the Ecoinvent Database version 3 part ii analyzing lca results and comparison to version 2
International Journal of Life Cycle Assessment, 2016Co-Authors: Jürgen Reinhard, Gregor Wernet, Bernhard Steubing, Christian Bauer, Emilia MorenoruizAbstract:Version 3 of Ecoinvent includes more data, new modeling principles, and, for the first time, several system models: the “Allocation, cut-off by classification” (Cut-off) system model, which replicates the modeling principles of version 2, and two newly introduced models called “Allocation at the point of substitution” (APOS) and “Consequential” (Wernet et al. 2016). The aim of this paper is to analyze and explain the differences in life cycle impact assessment (LCIA) results of the v3.1 Cut-off system model in comparison to v2.2 as well as the APOS and Consequential system models. In order to do this, functionally equivalent datasets were matched across Database versions and LCIA results compared to each other. In addition, the contribution of specific sectors was analyzed. The importance of new and updated data as well as new modeling principles is illustrated through examples. Differences were observed in between all Database versions using the impact assessment methods Global Warming Potential (GWP100a), ReCiPe Endpoint (H/A), and Ecological Scarcity 2006 (ES’06). The highest differences were found for the comparison of the v3.1 Cut-off and v2.2. At average, LCIA results increased by 6, 8, and 17 % and showed a median dataset deviation of 13, 13, and 21 % for GWP, ReCiPe, and ES’06, respectively. These changes are due to the simultaneous update and addition of new data as well as through the introduction of global coverage and spatially consistent linking of activities throughout the Database. As a consequence, supply chains are now globally better represented than in version 2 and lead, e.g., in the electricity sector, to more realistic life cycle inventory (LCI) background data. LCIA results of the Cut-off and APOS models are similar and differ mainly for recycling materials and wastes. In contrast, LCIA results of the Consequential version differ notably from the attributional system models, which is to be expected due to fundamentally different modeling principles. The use of marginal instead of average suppliers in markets, i.e., consumption mixes, is the main driver for result differences. LCIA results continue to change as LCI Databases evolve, which is confirmed by a historical comparison of v1.3 and v2.2. Version 3 features more up-to-date background data as well as global supply chains and should, therefore, be used instead of previous versions. Continuous efforts will be required to decrease the contribution of Rest-of-the-World (RoW) productions and thereby improve the global coverage of supply chains.
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International Journal of Life Cycle Assessment - The Ecoinvent Database version 3 (part II): analyzing LCA results and comparison to version 2
The International Journal of Life Cycle Assessment, 2016Co-Authors: Bernhard Steubing, Gregor Wernet, Jürgen Reinhard, Christian Bauer, Emilia Moreno-ruizAbstract:Version 3 of Ecoinvent includes more data, new modeling principles, and, for the first time, several system models: the “Allocation, cut-off by classification” (Cut-off) system model, which replicates the modeling principles of version 2, and two newly introduced models called “Allocation at the point of substitution” (APOS) and “Consequential” (Wernet et al. 2016). The aim of this paper is to analyze and explain the differences in life cycle impact assessment (LCIA) results of the v3.1 Cut-off system model in comparison to v2.2 as well as the APOS and Consequential system models. In order to do this, functionally equivalent datasets were matched across Database versions and LCIA results compared to each other. In addition, the contribution of specific sectors was analyzed. The importance of new and updated data as well as new modeling principles is illustrated through examples. Differences were observed in between all Database versions using the impact assessment methods Global Warming Potential (GWP100a), ReCiPe Endpoint (H/A), and Ecological Scarcity 2006 (ES’06). The highest differences were found for the comparison of the v3.1 Cut-off and v2.2. At average, LCIA results increased by 6, 8, and 17 % and showed a median dataset deviation of 13, 13, and 21 % for GWP, ReCiPe, and ES’06, respectively. These changes are due to the simultaneous update and addition of new data as well as through the introduction of global coverage and spatially consistent linking of activities throughout the Database. As a consequence, supply chains are now globally better represented than in version 2 and lead, e.g., in the electricity sector, to more realistic life cycle inventory (LCI) background data. LCIA results of the Cut-off and APOS models are similar and differ mainly for recycling materials and wastes. In contrast, LCIA results of the Consequential version differ notably from the attributional system models, which is to be expected due to fundamentally different modeling principles. The use of marginal instead of average suppliers in markets, i.e., consumption mixes, is the main driver for result differences. LCIA results continue to change as LCI Databases evolve, which is confirmed by a historical comparison of v1.3 and v2.2. Version 3 features more up-to-date background data as well as global supply chains and should, therefore, be used instead of previous versions. Continuous efforts will be required to decrease the contribution of Rest-of-the-World (RoW) productions and thereby improve the global coverage of supply chains.
Bo Pedersen Weidema - One of the best experts on this subject based on the ideXlab platform.
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Estimation of the size of error introduced into consequential models by using attributional background datasets
International Journal of Life Cycle Assessment, 2016Co-Authors: Bo Pedersen WeidemaAbstract:Purpose A systematic comparison is made of attributional and consequential results for the same products using the same unit process Database, thus isolating the effect of the two system models. An analysis of this nature has only recently been made possible due to the Ecoinvent Database version 3 providing an access to both unallocated and unlinked unit process datasets as well as both attributional and consequential models based on these datasets. The analysis is therefore limited to the system models provided by Ecoinvent.
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The Ecoinvent Database version 3 (part I): overview and methodology
The International Journal of Life Cycle Assessment, 2016Co-Authors: Gregor Wernet, Emilia Moreno-ruiz, Jürgen Reinhard, Bernhard Steubing, Christian Bauer, Bo Pedersen WeidemaAbstract:Purpose Good background data are an important requirement in LCA. Practitioners generally make use of LCI Databases for such data, and the Ecoinvent Database is the largest transparent unit-process LCI Database worldwide. Since its first release in 2003, it has been continuously updated, and version 3 was published in 2013. The release of version 3 introduced several significant methodological and technological improvements, besides a large number of new and updated datasets. The aim was to expand the content of the Database, set the foundation for a truly global Database, support regionalized LCIA, offer multiple system models, allow for easier integration of data from different regions, and reduce maintenance efforts. This article describes the methodological developments. Methods Modeling choices and raw data were separated in version 3, which enables the application of different sets of modeling choices, or system models, to the same raw data with little effort. This includes one system model for Consequential LCA. Flow properties were added to all exchanges in the Database, giving more information on the inventory and allowing a fast calculation of mass and other balances. With version 3.1, the Database is generally water-balanced, and water use and consumption can be determined. Consumption mixes called market datasets were consistently added to the Database, and global background data was added, often as an extrapolation from regional data. Results and discussion In combination with hundreds of new unit processes from regions outside Europe, these changes lead to an improved modeling of global supply chains, and a more realistic distribution of impacts in regionalized LCIA. The new mixes also facilitate further regionalization due to the availability of background data for all regions. Conclusions With version 3, the Ecoinvent Database substantially expands the goals and scopes of LCA studies it can support. The new system models allow new, different studies to be performed. Global supply chains and market datasets significantly increase the relevance of the Database outside of Europe, and regionalized LCA is supported by the data. Datasets are more transparent, include more information, and support, e.g., water balances. The developments also support easier collaboration with other Database initiatives, as demonstrated by a first successful collaboration with a data project in Québec. Version 3 has set the foundation for expanding Ecoinvent from a mostly regional into a truly global Database and offers many new insights beyond the thousands of new and updated datasets it also introduced.
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the Ecoinvent Database version 3 part i overview and methodology
International Journal of Life Cycle Assessment, 2016Co-Authors: Gregor Wernet, Jürgen Reinhard, Emilia Morenoruiz, Bernhard Steubing, Christian Bauer, Bo Pedersen WeidemaAbstract:Purpose Good background data are an important requirement in LCA. Practitioners generally make use of LCI Databases for such data, and the Ecoinvent Database is the largest transparent unit-process LCI Database worldwide. Since its first release in 2003, it has been continuously updated, and version 3 was published in 2013. The release of version 3 introduced several significant methodological and technological improvements, besides a large number of new and updated datasets. The aim was to expand the content of the Database, set the foundation for a truly global Database, support regionalized LCIA, offer multiple system models, allow for easier integration of data from different regions, and reduce maintenance efforts. This article describes the methodological developments.
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Reducing Impacts of Forestry: The Fallacy of Low-Intensity Management
2013Co-Authors: Bo Pedersen WeidemaAbstract:New definitions are provided of intensive and extensive forestry in version 3 of the Ecoinvent Database. These definitions are based on explicit and easily measured indicators for the most important aspects of forestry management for biodiversity. Unfortunately, many certified forestry products come from what would be classified as intensive forestry in the Ecoinvent classification. The real challenge is to develop forest management systems that have a neutral or positive biodiversity impact relative to that of plantation forestry. Such truly extensive, biodiversity-managed forestry is very challenging and not very common today. Ample options exist for increasing yields in intensive and plantation forests, which can be recommended as having lower biodiversity impact than similar products from other management systems, certified or not.
Jürgen Reinhard - One of the best experts on this subject based on the ideXlab platform.
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Integrating regionalized Brazilian land use change datasets into the Ecoinvent Database: new data, premises and uncertainties have large effects in the results
The International Journal of Life Cycle Assessment, 2020Co-Authors: Ana Cristina Guimarães Donke, Emilia Moreno-ruiz, Renan Milagres Lage Novaes, Ricardo Antonio Almeida Pazianotto, Jürgen Reinhard, Juliana Ferreira Picoli, Marília Ieda Da Silveira Folegatti-matsuuraAbstract:Purpose Land use change (LUC) is a critical process in the life cycle greenhouse gas emissions of agricultural products and Brazil is a major exporter of these. This work had the objective of integrating refined and regionalized datasets of LUC in Brazil into the Ecoinvent Database, to better represent its dynamics and heterogeneity. We present the adaptations needs for having it suitable for crops, pasture and forestry in state-level and impacts of modelling assumptions and uncertainties. Methods Adaptation and integration were based in Ecoinvent version 3 guidelines and the Database requirements to LUC modelling. BRLUC, a method for Brazilian LUC accounting, was the main data source. The workflow for the integration process consisted in identifying necessary adaptations in both sources to allow a better representation of Brazilian LUC. Four new reference products and 27 geographies were added in the Database. Results and discussion A total of 566 new datasets were integrated into Ecoinvent version 3.6, allowing the incorporation of LUC in Brazilian products in state, regional and national level. GHG emissions reduced, being 42.2% and 99.9% lower to soybean and sugarcane than in Ecoinvent v3.5. Four improvements were the main causes: (i) state-level LUC modelling with national official data; (ii) regionalizing carbon stocks; (iii) including pasture and forestry land use categories; (iv) and considering sugarcane as a perennial crop. The way to calculate national-level results based on subnational data was an important source of difference in emissions too. Uncertainties specifically associated with land use substitution patterns were not incorporated, and they can potentially have impacts as large as the uncertainties of all the remaining processes combined. Conclusions Results showed that small changes in data sources and premises have large impacts on emissions associated with LUC in agricultural products. It also showed the large impacts of uncertainties of LUC patterns. Improving current models in better representing regional LUC patterns, regional carbon stocks and uncertainty accounting could reduce these impacts. Nonetheless, efforts in reducing the complexity of LUC accounting methods could enhance transparency and effectiveness.
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The Ecoinvent Database version 3 (part I): overview and methodology
The International Journal of Life Cycle Assessment, 2016Co-Authors: Gregor Wernet, Emilia Moreno-ruiz, Jürgen Reinhard, Bernhard Steubing, Christian Bauer, Bo Pedersen WeidemaAbstract:Purpose Good background data are an important requirement in LCA. Practitioners generally make use of LCI Databases for such data, and the Ecoinvent Database is the largest transparent unit-process LCI Database worldwide. Since its first release in 2003, it has been continuously updated, and version 3 was published in 2013. The release of version 3 introduced several significant methodological and technological improvements, besides a large number of new and updated datasets. The aim was to expand the content of the Database, set the foundation for a truly global Database, support regionalized LCIA, offer multiple system models, allow for easier integration of data from different regions, and reduce maintenance efforts. This article describes the methodological developments. Methods Modeling choices and raw data were separated in version 3, which enables the application of different sets of modeling choices, or system models, to the same raw data with little effort. This includes one system model for Consequential LCA. Flow properties were added to all exchanges in the Database, giving more information on the inventory and allowing a fast calculation of mass and other balances. With version 3.1, the Database is generally water-balanced, and water use and consumption can be determined. Consumption mixes called market datasets were consistently added to the Database, and global background data was added, often as an extrapolation from regional data. Results and discussion In combination with hundreds of new unit processes from regions outside Europe, these changes lead to an improved modeling of global supply chains, and a more realistic distribution of impacts in regionalized LCIA. The new mixes also facilitate further regionalization due to the availability of background data for all regions. Conclusions With version 3, the Ecoinvent Database substantially expands the goals and scopes of LCA studies it can support. The new system models allow new, different studies to be performed. Global supply chains and market datasets significantly increase the relevance of the Database outside of Europe, and regionalized LCA is supported by the data. Datasets are more transparent, include more information, and support, e.g., water balances. The developments also support easier collaboration with other Database initiatives, as demonstrated by a first successful collaboration with a data project in Québec. Version 3 has set the foundation for expanding Ecoinvent from a mostly regional into a truly global Database and offers many new insights beyond the thousands of new and updated datasets it also introduced.
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the Ecoinvent Database version 3 part i overview and methodology
International Journal of Life Cycle Assessment, 2016Co-Authors: Gregor Wernet, Jürgen Reinhard, Emilia Morenoruiz, Bernhard Steubing, Christian Bauer, Bo Pedersen WeidemaAbstract:Purpose Good background data are an important requirement in LCA. Practitioners generally make use of LCI Databases for such data, and the Ecoinvent Database is the largest transparent unit-process LCI Database worldwide. Since its first release in 2003, it has been continuously updated, and version 3 was published in 2013. The release of version 3 introduced several significant methodological and technological improvements, besides a large number of new and updated datasets. The aim was to expand the content of the Database, set the foundation for a truly global Database, support regionalized LCIA, offer multiple system models, allow for easier integration of data from different regions, and reduce maintenance efforts. This article describes the methodological developments.
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the Ecoinvent Database version 3 part ii analyzing lca results and comparison to version 2
International Journal of Life Cycle Assessment, 2016Co-Authors: Jürgen Reinhard, Gregor Wernet, Bernhard Steubing, Christian Bauer, Emilia MorenoruizAbstract:Version 3 of Ecoinvent includes more data, new modeling principles, and, for the first time, several system models: the “Allocation, cut-off by classification” (Cut-off) system model, which replicates the modeling principles of version 2, and two newly introduced models called “Allocation at the point of substitution” (APOS) and “Consequential” (Wernet et al. 2016). The aim of this paper is to analyze and explain the differences in life cycle impact assessment (LCIA) results of the v3.1 Cut-off system model in comparison to v2.2 as well as the APOS and Consequential system models. In order to do this, functionally equivalent datasets were matched across Database versions and LCIA results compared to each other. In addition, the contribution of specific sectors was analyzed. The importance of new and updated data as well as new modeling principles is illustrated through examples. Differences were observed in between all Database versions using the impact assessment methods Global Warming Potential (GWP100a), ReCiPe Endpoint (H/A), and Ecological Scarcity 2006 (ES’06). The highest differences were found for the comparison of the v3.1 Cut-off and v2.2. At average, LCIA results increased by 6, 8, and 17 % and showed a median dataset deviation of 13, 13, and 21 % for GWP, ReCiPe, and ES’06, respectively. These changes are due to the simultaneous update and addition of new data as well as through the introduction of global coverage and spatially consistent linking of activities throughout the Database. As a consequence, supply chains are now globally better represented than in version 2 and lead, e.g., in the electricity sector, to more realistic life cycle inventory (LCI) background data. LCIA results of the Cut-off and APOS models are similar and differ mainly for recycling materials and wastes. In contrast, LCIA results of the Consequential version differ notably from the attributional system models, which is to be expected due to fundamentally different modeling principles. The use of marginal instead of average suppliers in markets, i.e., consumption mixes, is the main driver for result differences. LCIA results continue to change as LCI Databases evolve, which is confirmed by a historical comparison of v1.3 and v2.2. Version 3 features more up-to-date background data as well as global supply chains and should, therefore, be used instead of previous versions. Continuous efforts will be required to decrease the contribution of Rest-of-the-World (RoW) productions and thereby improve the global coverage of supply chains.
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International Journal of Life Cycle Assessment - The Ecoinvent Database version 3 (part II): analyzing LCA results and comparison to version 2
The International Journal of Life Cycle Assessment, 2016Co-Authors: Bernhard Steubing, Gregor Wernet, Jürgen Reinhard, Christian Bauer, Emilia Moreno-ruizAbstract:Version 3 of Ecoinvent includes more data, new modeling principles, and, for the first time, several system models: the “Allocation, cut-off by classification” (Cut-off) system model, which replicates the modeling principles of version 2, and two newly introduced models called “Allocation at the point of substitution” (APOS) and “Consequential” (Wernet et al. 2016). The aim of this paper is to analyze and explain the differences in life cycle impact assessment (LCIA) results of the v3.1 Cut-off system model in comparison to v2.2 as well as the APOS and Consequential system models. In order to do this, functionally equivalent datasets were matched across Database versions and LCIA results compared to each other. In addition, the contribution of specific sectors was analyzed. The importance of new and updated data as well as new modeling principles is illustrated through examples. Differences were observed in between all Database versions using the impact assessment methods Global Warming Potential (GWP100a), ReCiPe Endpoint (H/A), and Ecological Scarcity 2006 (ES’06). The highest differences were found for the comparison of the v3.1 Cut-off and v2.2. At average, LCIA results increased by 6, 8, and 17 % and showed a median dataset deviation of 13, 13, and 21 % for GWP, ReCiPe, and ES’06, respectively. These changes are due to the simultaneous update and addition of new data as well as through the introduction of global coverage and spatially consistent linking of activities throughout the Database. As a consequence, supply chains are now globally better represented than in version 2 and lead, e.g., in the electricity sector, to more realistic life cycle inventory (LCI) background data. LCIA results of the Cut-off and APOS models are similar and differ mainly for recycling materials and wastes. In contrast, LCIA results of the Consequential version differ notably from the attributional system models, which is to be expected due to fundamentally different modeling principles. The use of marginal instead of average suppliers in markets, i.e., consumption mixes, is the main driver for result differences. LCIA results continue to change as LCI Databases evolve, which is confirmed by a historical comparison of v1.3 and v2.2. Version 3 features more up-to-date background data as well as global supply chains and should, therefore, be used instead of previous versions. Continuous efforts will be required to decrease the contribution of Rest-of-the-World (RoW) productions and thereby improve the global coverage of supply chains.
