The Quebec Life Cycle Inventory Database Project

Purpose

Life cycle assessment (LCA) in Quebec (Canada) is increasingly important. Yet, studies often still need to rely on foreign life cycle inventory (LCI) data. The Quebec government invested in the creation of a Quebec LCI database. The approach is to work as an ecoinvent “National Database Initiative” (NDI), whereby the Quebec database initiative uses and contributes to the ecoinvent database. The paper clarifies the relationship between ecoinvent and the Quebec NDI and provides details on prioritization and data collection.

Methods

The first steps were to select a partner database provider and to work out the modalities of the partnership. The main criterion for partner selection was database transparency, i.e., availability of unit process data (gate-to-gate), necessary for database adaptation. This and other criteria, such as free access to external reviewers, conservation of dataset copyright, seamless embedding of datasets, and overall database sophistication, pointed to ecoinvent. Once started, the NDI project proceeded as follows: (1) data collection was prioritized based on several criteria; (2) some datasets were “recontextualized,” i.e., existing datasets were duplicated and relocated in Quebec and linked to datasets representing regional suppliers, where relevant; (3) new datasets were created; and (4) Canadian environmentally extended supply-use tables were created for the ecoinvent IO repository.

Results and discussion

Prioritization identified 500 candidate datasets for recontextualization, based on the relative importance of relative contribution of direct electricity consumption to cradle-to-gate impacts, and 12 key sectors from which about 450 data adaptation or collection projects were singled out. Data collection and private sector solicitation are underway. Private sector participation is highly variable. A number of communication tools have been elaborated and a solicitation team formed to palliate this obstacle. The new ecoinvent database protocol (Weidema et al. 2011) increases the amount of information that is required to create a dataset, which can lengthen or, in extreme cases, impede dataset creation. However, this new information is required for the new database functionalities (e.g., providing multiple system models based on the same unit process data and regionalized LCA).

Conclusions

Being an NDI is advantageous for the Quebec LCI database project on multiple levels. By conserving dataset copyright, the NDI remains free to spawn or support other LCI databases. Embedding datasets in ecoinvent enables the generation of LCI results from “day 1.” The costs of IT infrastructure and data review are null. For these reasons, and because every NDI improves the global representativity of ecoinvent, we recommend other regional or national database projects work as NDIs.

     

Input‐Output‐based Life Cycle Inventory

An input‐output‐based life cycle inventory (IO‐based LCI) is grounded on economic environmental input‐output analysis (IO analysis). It is a fast and low‐budget method for generating LCI data sets, and is used to close data gaps in life cycle assessment (LCA). Due to the fact that its methodological basis differs from that of process‐based inventory, its application in LCA is a matter of controversy. We developed a German IO‐based approach to derive IO‐based LCI data sets that is based on the German IO accounts and on the German environmental accounts, which provide data for the sector‐specific direct emissions of seven airborne compounds. The method to calculate German IO‐based LCI data sets for building products is explained in detail. The appropriateness of employing IO‐based LCI for German buildings is analyzed by using process‐based LCI data from the Swiss Ecoinvent database to validate the calculated IO‐based LCI data. The extent of the deviations between process‐based LCI and IO‐based LCI varies considerably for the airborne emissions we investigated. We carried out a systematic evaluation of the possible reasons for this deviation. This analysis shows that the sector‐specific effects (aggregation of sectors) and the quality of primary data for emissions from national inventory reporting (NIR) are the main reasons for the deviations. As a rule, IO‐based LCI data sets seem to underestimate specific emissions while overestimating sector‐specific aspects.     

Life cycle inventories of electricity generation and power supply in version 3 of the ecoinvent database—part II: electricity markets

Purpose

Representative, consistent and up-to-date life cycle inventories (LCI) of electricity supply are key elements of ecoinvent as an LCI background database since these are often among the determining factors with regard to life cycle assessment (LCA) results. ecoinvent version 3 (ev3) offers new LCI data of power supply (electricity markets) in 71 geographies. This article gives an overview of these electricity markets and discusses new ecoinvent features in the context of power supply.

Methods

The annual geography- and technology-specific electricity production for the year 2008 specifies the technology shares on the high-, medium- and low-voltage level electricity markets. Data are based on IEA statistics. Different voltage levels are linked by transformation activities. Region-specific electricity losses due to power transmission and voltage transformation are considered in the market and transformation activities. The majority of the 71 power markets are defined by national boundaries. The attributional ecoinvent system model in ev3 with linking to average current suppliers results in electricity markets supplied by all geography-specific power generation technologies and electricity imports, while the consequential system model generates markets only linked to unconstrained suppliers.

Results and discussion

The availability of LCI data for 71 electricity markets in ev3 covering 50 countries reduces the “Rest-of-the-World” electricity supply not covered by country- or region-specific inventories to 17 % for the year 2008. Specific power supply activities for all countries contributing more than 1 % to global electricity production are available. The electricity markets show large variations concerning contributions from specific technologies and energy carriers. Imports can substantially change the national/regional power mix, especially in small markets. Large differences can also be observed between the electricity markets in the attributional and the consequential database calculation. Region-specific total power losses between production on the high voltage level and consumer on the low voltage level are on the order of 2.5–23 %.

Conclusions

Electricity supply mixes (electricity markets) in the ecoinvent database have been updated and substantially extended for v3. Inventories for electricity supply in all globally important economies are available with geography-specific technology and market datasets which will contribute to increasing quality and reducing uncertainties in LCA studies worldwide and to allow more accurate estimation of environmental burdens from global production chains. Future work should focus on improving the details of country-specific data, implementation of more countries into the database, splitting of large countries into smaller regions and on developing a more sophisticated approach specifying country-specific electricity mixes in consequential system models.

     

A trade-based method for modelling supply markets in consequential LCA exemplified with Portland cement and bananas

Purpose

This study proposes a method based on the analysis of trade networks over time for modelling the marginal supply of products in consequential life cycle assessment (LCA). It aims at increasing the geographical granularity of markets, accuracy of transport distances and modes and material losses during transit by creating country-specific markets, instead of region-based supply-origin markets as currently proposed by ecoinvent. It leads to a better consideration of the environmental weight of trade following a change in demand on a local market and may serve as an inspirational basis for future releases of consequential life cycle inventory (LCI) databases.

Methods

The method uses ecoinvent v.3.3 as a support LCI database and two distinct traded products: bananas and grey Portland cement. Each country involved in the trade of a said product has a corresponding market created in the LCI database. The behavior of market to a marginal change in internal demand is modelled after its marginal trading preferences: it can either affect local production, imports, exports or a mix of the first two. Markets are linked to one another based on the linear regression analysis of their historical trade relations. The inventories that follow an increase in demand of 1000 kg of bananas and grey Portland cement are calculated for each market involved in their trade and are environmentally characterized and compared to the generic region-based market datasets provided by ecoinvent to assess the gains in accuracy through a higher geographical granularity. Furthermore, the characterized inventories of the markets for bananas are compared to a parallel scenario where transport distances are kept to a minimum using the shortest path method. It isolates the environmental burden associated to the utility maximization of the demand.

Results and discussion

When comparing the characterized impacts of country-specific markets with the generic ecoinvent market datasets, disparities in results appear. They highlight the importance of transport induced by demand displacement and losses of material during transport, both being the consequences of the extent a given market decides to be supplied directly from producing markets at the margin. These are aspects that may go unaccounted for when using generic regional markets. Second, optimizing transport distances for each market decreases the environmental impacts for most categories by more than 70%.

Conclusions

This study shows there is a need for modelling and understanding market relations to more accurately define the role of trade, supply chain efficiency and import policies in LCA.

     

Establishing Life Cycle Inventories of Chemicals Based on Differing Data Availability (9 pp)

Goal, Scope and Background In contrast to inventory data of energy and transport processes, public inventory data of chemicals are rather scarce. Chemicals are important to consider in LCA, because they are used in the production of many, if not all, products. Moreover, they may cause considerable environmental impacts. For these reasons, it was one goal of the new ecoinvent database to provide LCI data on chemicals. In this paper, the methods and procedures used for establishing LCIs of chemicals in ecoinvent are presented.Methods Three different approaches are suggested for situations of differing data availability. First, in the case of good data availability, the general quality guidelines of ecoinvent can be followed. Second, a procedure is proposed for the translation of aggregated inventory data (cumulative LCI results) from industry into the ecoinvent format. This approach was used, if adequate unit process data was not available. Third, a procedure is put forward for estimating inventory data using stoichiometric equations from technical literature as a main information source. This latter method was used if no other information was available. The application of each of the three procedures is illustrated with the help of a case study.Results and Conclusion When sufficient information is available to follow the general guidelines of ecoinvent, the resulting dataset is characterized by a high degree of detail, and it is thus of high quality. For chemicals, however, the application of the standard procedure is possible in only a few cases. When using industrial data, the main drawback is the fact that those data are often available only as aggregated data, thus being out of tune with the quality guidelines of ecoinvent and its main aim, the harmonization of LCI data. As a third approach, the use of the stoichiometric reaction equation is used for the compilation of LCI datasets of chemicals. This approach represents an alternative to neglecting chemicals completely, but it contains a high risk to not consider important aspects of the life cycle of the respective substance.Outlook Further work in the area of chemicals should focus on an improvement of datasets, so far established by either of the two estimation procedures (APME method; estimation based on technical literature) described. Besides the improvement of already established inventories, the compilation of further harmonized inventories of specific types of chemicals (e.g. solvents) or of chemicals for new industrial sectors (e.g. electronics industry) are in discussion.     

Waste Treatment and Assessment of Long-Term Emissions (8pp)

Goal, Scope and Background The disposal phase of a product’s life cycle in LCA is often neglected or based on coarse indicators like ‘kilogram waste’. The goal of report No. 13 of the ecoinvent project (Doka 2003) is to create detailed Life Cycle Inventories of waste disposal processes. The purpose of this paper is to give an overview of the models behind the waste disposal inventories in ecoinvent, to present exemplary results and to discuss the assessment of long-term emissions. This paper does not present a particular LCA study. Inventories are compiled for many different materials and various disposal technologies. Considered disposal technologies are municipal incineration and different landfill types, including sanitary landfills, hazardous waste incineration, waste deposits in deep salt mines, surface spreading of sludges, municipal wastewater treatment, and building dismantling. The inventoried technologies are largely based on Swiss plants. Inventories can be used for assessment of the disposal of common, generic waste materials like paper, plastics, packaging etc. Inventories are also used within the ecoinvent database itself to inventory the disposal of specific wastes generated during the production phase. Inventories relate as far as possible to the specific chemical composition of the waste material (waste-specific burdens). Certain expenditures are not related to the waste composition and are inventoried with average values (process-specific burdens). Methods The disposal models are based on previous work, partly used in earlier versions of ecoinvent/ETH LCI data. Important improvements were the extension of the number of considered chemical elements to 41 throughout all disposal models and new landfill models based on field data. New inventories are compiled for waste deposits in deep salt mines and building material disposal. Along with the ecoinvent data and the reports, also Excel-based software tools were created, which allow ecoinvent members to calculate waste disposal inventories from arbitrary waste compositions. The modelling of long-term emissions from landfills is a crucial part in any waste disposal process. In ecoinvent long-term emissions are defined as emissions occurring 100 years after present. They are reported in separate emission categories. The landfill inventories include long-term emissions with a time horizon of 60’000 years after present. Results and Discussion As in earlier studies, the landfills prove to be generally relevant disposal processes, as also incineration and wastewater treatment processes produce landfilled wastes. Heavy metals tend to concentrate in landfills and are washed out to a varying degree over time. Long-term emissions usually represent an important burden from landfills. Comparisons between burdens from production of materials and the burdens from their disposal show that disposal has a certain relevance. Conclusion The disposal phase should by default be included in LCA studies. The use of a material not only necessitates its production, but also requires its disposal. The created inventories and user tools facilitate heeding the disposal phase with a similar level of detail as production processes. The risk of LCA-based decisions shifting burdens from the production or use phase to the disposal phase because of data gaps can therefore be diminished. Recommendation and Perspective Future improvements should include the modelling of metal ore refining waste (tailings) which is currently neglected in ecoinvent, but is likely to be relevant for metals production. The disposal technologies considered here are those of developed Western countries. Disposal in other parts of the World can differ distinctly, for logistic, climatic and economic reasons. The cross-examination of landfill models to LCIA soil fate models could be advantageous. Currently only chemical elements, like copper, zinc, nitrogen etc. are heeded by the disposal models. A possible extension could be the modelling of the behaviour of chemical compounds, like dioxins or other hydrocarbons.     

Life cycle assessment of the Peruvian industrial anchoveta fleet: boundary setting in life cycle inventory analyses of complex and plural means of production

Purpose

This work has two major objectives: (1) to perform an attributional life cycle assessment (LCA) of a complex mean of production, the main Peruvian fishery targeting anchoveta (anchovy) and (2) to assess common assumptions regarding the exclusion of items from the life cycle inventory (LCI).

Methods

Data were compiled for 136 vessels of the 661 units in the fleet. The functional unit was 1 t of fresh fish delivered by a steel vessel. Our approach consisted of four steps: (1) a stratified sampling scheme based on a typology of the fleet, (2) a large and very detailed inventory on small representative samples with very limited exclusion based on conventional LCI approaches, (3) an impact assessment on this detailed LCI, followed by a boundary-refining process consisting of retention of items that contributed to the first 95 % of total impacts and (4) increasing the initial sample with a limited number of items, according to the results of (3). The life cycle impact assessment (LCIA) method mostly used was ReCiPe v1.07 associated to the ecoinvent database.

Results and discussion

Some items that are usually ignored in an LCI’s means of production have a significant impact. The use phase is the most important in terms of impacts (66 %), and within that phase, fuel consumption is the leading inventory item contributing to impacts (99 %). Provision of metals (with special attention to electric wiring which is often overlooked) during construction and maintenance, and of nylon for fishing nets, follows. The anchoveta fishery is shown to display the lowest fuel use intensity worldwide.

Conclusions

Boundary setting is crucial to avoid underestimation of environmental impacts of complex means of production. The construction, maintenance and EOL stages of the life cycle of fishing vessels have here a substantial environmental impact. Recommendations can be made to decrease the environmental impact of the fleet.     

Life Cycle Assessment for Emerging Technologies: Case Studies for Photovoltaic and Wind Power (11 pp)

Goal, Scope and Background This paper describes the modelling of two emerging electricity systems based on renewable energy: photovoltaic (PV) and wind power. The paper shows the approach used in the ecoinvent database for multi-output processes.Methods Twelve different, grid-connected photovoltaic systems were studied for the situation in Switzerland. They are manufactured as panels or laminates, from mono- or polycrystalline silicon, installed on facades, slanted or flat roofs, and have a 3kWp capacity. The process data include quartz reduction, silicon purification, wafer, panel and laminate production, supporting structure and dismantling. The assumed operational lifetime is 30 years. Country-specific electricity mixes have been considered in the LCI in order to reflect the present situation for individual production stages. The assessment of wind power includes four different wind turbines with power rates between 30 kW and 800 kW operating in Switzerland and two wind turbines assumed representative for European conditions – 800 kW onshore and 2 MW offshore. The inventory takes into account the construction of the plants including the connection to the electric grid and the actual wind conditions at each site in Switzerland. Average European capacity factors have been assumed for the European plants. Eventually necessary backup electricity systems are not included in the analysis.Results and Discussion The life cycle inventory analysis for photovoltaic power shows that each production stage may be important for specific elementary flows. A life cycle impact assessment (LCIA) shows that there are important environmental impacts not directly related to the energy use (e.g. process emissions of NOx from wafer etching). The assumption for the used supply energy mixes is important for the overall LCIA results of different production stages. The allocation of the inventory for silicon purification to different products is discussed here to illustrate how allocation has been implemented in ecoinvent. Material consumption for the main parts of the wind turbines gives the dominant contributions to the cumulative results for electricity production. The complex installation of offshore turbines, with high requirements of concrete for the foundation and the assumption of a shorter lifetime compared to onshore foundations, compensate the advantage of increased offshore wind speeds.Conclusion The life cycle inventories for photovoltaic power plants are representative for newly constructed plants and for the average photovoltaic mix in Switzerland in the year 2000. A scenario for a future technology helps to assess the relative influence of technology improvements for some processes in the near future (2005-2010). The differences for environmental burdens of wind power basically depend upon the capacity factor of the plants, the lifetime of the infrastructure, and the rated power. The higher these factors, the more reduced the environmental burdens are. Thus, both systems are quite dependent on meteorological conditions and the materials used for the infrastructure.Recommendation and Perspective Many production processes for photovoltaic power are still under development. Future updates of the LCI should verify the energy uses and emissions with available data from industrial processes in operation. For the modelling of a specific power plant or power plant mixes outside of Switzerland, one has to consider the annual yield (kWh/kWp) and if possible also the size of the plant. Considering the steady growth of the size of wind turbines in Europe, the development of new designs, and the exploitation of offshore location with deeper waters than analysed in this study, the inventory for wind power plants may need to be updated in the future.     

Temporal differentiation of background systems in LCA: relevance of adding temporal information in LCI databases

Purpose

Because the potential impacts of emissions and extractions can be sensitive to timing, the temporal aggregation of life cycle inventory (LCI) data has often been cited as a limitation in life cycle assessment (LCA). Until now, examples of temporal emission and extraction distributions were restricted to the foreground processes of product systems. The objective of this paper is to evaluate the relevance of considering the temporal distribution of the background system inventory.

Methods

The paper focuses on the global warming impact category for which so-called dynamic characterization factors (CFs) were developed and uses the ecoinvent v2.2 database as both an example database to which temporal information can be added and a source of product systems to test the relevance of adding temporal information to the background system. Temporal information was added to the elementary and intermediate exchanges of 22 % of the unit processes in the database. Using the enhanced structure path analysis (ESPA) method to generate temporally differentiated LCIs in conjunction with time-dependent global warming characterization factors, potential impacts were calculated for all 4,034 product systems in the ecoinvent database.

Results and discussion

Each time, the results were calculated for (1) systems in which temporal information was only added to the first two tiers, representing studies in which only the foreground system is temporally differentiated, and (2) systems in which temporal information was also added to the background system. For 8.6 % of the database product systems, adding temporal differentiation to background unit processes affected the global warming impact scores by more than 10 %. For most of the affected product systems, considering temporal information in the background unit processes decreased the global warming impact scores. The sectors that show most sensitivity to the temporal differentiation of background unit processes are associated with wood and biofuel sectors.

Conclusions

Even though the addition of temporal information to unit processes in LCI databases would not benefit every LCA study, the enhancement can be relevant. It allows for a more accurate global warming impact assessment, especially for LCAs in which products of biomass are present in substantial amounts. Relevance for other impact categories could be discussed in further work.     

Parameterised inventories for life cycle assessment

Background and Objective  

Life cycle assessment (LCA) is a highly data intensive undertaking, where collecting the life cycle inventory (LCI) data is the most labour intensive part. The aim of this paper is to show a method for representing the LCI in a simplified manner which not only allows an estimative, quantitative LCA, but also the application of advanced analysis methods to LCA.     

Life cycle inventories of electricity generation and power supply in version 3 of the ecoinvent database—part I: electricity generation

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.

Methods

Methods 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 discussion

Datasets 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.

Conclusions

Inventories 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.

     

Using fuzzy numbers to propagate uncertainty in matrix-based LCI

Background, aim, and scope  Analysis of uncertainties plays a vital role in the interpretation of life cycle assessment findings. Some of these uncertainties arise from parametric data variability in life cycle inventory analysis. For instance, the efficiencies of manufacturing processes may vary among different industrial sites or geographic regions; or, in the case of new and unproven technologies, it is possible that prospective performance levels can only be estimated. Although such data variability is usually treated using a probabilistic framework, some recent work on the use of fuzzy sets or possibility theory has appeared in the literature. The latter school of thought is based on the notion that not all data variability can be properly described in terms of frequency of occurrence. In many cases, it is necessary to model the uncertainty associated with the subjective degree of plausibility of parameter values. Fuzzy set theory is appropriate for such uncertainties. However, the computations required for handling fuzzy quantities has not been fully integrated with the formal matrix-based life cycle inventory analysis (LCI) described by Heijungs and Suh (2002). Materials and methods  This paper integrates computations with fuzzy numbers into the matrix-based LCI computational model described in the literature. The approach uses fuzzy numbers to propagate the data variability in LCI calculations, and results in fuzzy distributions of the inventory results. The approach is developed based on similarities with the fuzzy economic input–output (EIO) model proposed by Buckley (Eur J Oper Res 39:54–60, 1989). Results  The matrix-based fuzzy LCI model is illustrated using three simple case studies. The first case shows how fuzzy inventory results arise in simple systems with variability in industrial efficiency and emissions data. The second case study illustrates how the model applies for life cycle systems with co-products, and thus requires the inclusion of displaced processes. The third case study demonstrates the use of the method in the context of comparing different carbon sequestration technologies. Discussion  These simple case studies illustrate the important features of the model, including possible computational issues that can arise with larger and more complex life cycle systems. Conclusions  A fuzzy matrix-based LCI model has been proposed. The model extends the conventional matrix-based LCI model to allow for computations with parametric data variability represented as fuzzy numbers. This approach is an alternative or complementary approach to interval analysis, probabilistic or Monte Carlo techniques. Recommendations and perspectives  Potential further work in this area includes extension of the fuzzy model to EIO-LCA models and to life cycle impact assessment (LCIA); development of hybrid fuzzy-probabilistic approaches; and integration with life cycle-based optimization or decision analysis. Additional theoretical work is needed for modeling correlations of the variability of parameters using interacting or correlated fuzzy numbers, which remains an unresolved computational issue. Furthermore, integration of the fuzzy model into LCA software can also be investigated.     

Moving towards an Egyptian national life cycle inventory database

Purpose

Life cycle inventory (LCI) data are region-specific because energy fuel mixtures and methods of production often differ from region to region. LCI database examples include US LCI, Ecoinvent v.2, and NIST, each of which is country-specific. Thus, the main aim of this study is to show that Egypt is in need of an Egyptian National LCI (ENLCI) database and to focus on the means of developing a database specific to Egypt.

Methods

Arab countries have thus far engaged in virtually no life cycle assessment (LCA) studies, and a significant neglect of this matter is in evidence for the continent of Africa and, in particular, Egypt. Thus, this study suggests an organizational and managerial framework for the development of a national LCI database and sheds light on the required LCI database categories and data quality for practical solutions reflecting who is equipped to do what in order to keep pace with the world.

Results

The results from this review are useful to standardize the study of the life cycle assessment concept in Egypt; to form a foundation for development of an Egyptian database for facilitating a cleaner environment; to encourage stakeholders, such as the environmental agencies, Egyptian Housing and Building Research Center, and the Ministry of Industry; to propose an organizational framework in which they play a central role; and to provide investment to initiate development.

Conclusions

The analysis indicates that the development of a LCI database specific to Egypt is difficult because Egypt has various technical and organizational challenges, but a roadmap of actions to be taken to move ahead is provided. The success of this roadmap depends on the capacity for developing the necessary technical and financial support and on strong partnerships with industry, government, LCA professionals, and academia.     

Framework for modelling data uncertainty in life cycle inventories

Modelling data uncertainty is not common practice in life cycle inventories (LCI), although different techniques are available for estimating and expressing uncertainties, and for propagating the uncertainties to the final model results. To clarify and stimulate the use of data uncertainty assessments in common LCI practice, the SETAC working group ‘Data Availability and Quality’ presents a framework for data uncertainty assessment in LCI. Data uncertainty is divided in two categories: (1) lack of data, further specified as complete lack of data (data gaps) and a lack of representative data, and (2) data inaccuracy. Filling data gaps can be done by input-output modelling, using information for similar products or the main ingredients of a product, and applying the law of mass conservation. Lack of temporal, geographical and further technological correlation between the data used and needed may be accounted for by applying uncertainty factors to the non-representative data. Stochastic modelling, which can be performed by Monte Carlo simulation, is a promising technique to deal with data inaccuracy in LCIs.     

Modeling process and material alternatives in life cycle assessments

Background, Aims and Scope  Although LCA is frequently used in product comparison, many practitioners are interested in identifying and assessing improvements within a life cycle. Thus, the goals of this work are to provide guidelines for scenario formulation for process and material alternatives within a life cycle inventory and to evaluate the usefulness of decision tree and matrix computational structures in the assessment of material and process alternatives. We assume that if the analysis goal is to guide the selection among alternatives towards reduced life cycle environmental impacts, then the analysis should estimate the inventory results in a manner that: (1) reveals the optimal set of processes with respect to minimization of each impact of interest, and (2) minimizes and organizes computational and data collection needs. Methods  A sample industrial system is used to reveal the complexities of scenario formulation for process and material alternatives in an LCI. The system includes 4 processes, each executable in 2 different ways, as well as 1 process able to use 2 different materials interchangeably. We formulate and evaluate scenarios for this system using three different methods and find advantages and disadvantages with each. First, the single branch decision tree method stays true to the typical construction of decision trees such that each branch of the tree represents a single scenario. Next, the process flow decision tree method strays from the typical construction of decision trees by following the process flow of the product system, such that multiple branches are needed to represent a single scenario. In the final method, disaggregating the demand vector, each scenario is represented by separate vectors which are combined into a matrix to allow the simultaneous solution of the inventory problem for all scenarios. Results  For both decision tree and matrix methods, scenario formulation, data collection, and scenario analysis are facilitated in two ways. First, process alternatives that cannot actually be chosen should be modeled as sub-inventories (or as a complete LCI within an LCI). Second, material alternatives (e.g., a choice between structural materials) must be maintained within the analysis to avoid the creation of artificial multi-functional processes. Further, in the same manner that decision trees can be used to estimate ‘expected value’ (the sum of the probability of each scenario multiplied by its ‘value’), we find that expected inventory and impact results can be defined for both decision tree and matrix methods. Discussion  For scenario formulation, naming scenarios in a way that differentiate them from other scenarios is complex and important in the continuing development of LCI data for use in databases or LCA software. In the formulation and assessment of scenarios, decision tree methods offer some level of visual appeal and the potential for using commercially available software/ traditional decision tree solution constructs for estimating expected values (for relatively small or highly aggregated product systems). However, solving decision tree systems requires the use of sequential process scaling which is difficult to formalize with mathematical notation. In contrast, preparation of a demand matrix does not require use of the sequential method to solve the inventory problem but requires careful scenario tracking efforts. Conclusions  Here, we recognize that improvements can be made within a product system. This recognition supports the greater use of LCA in supply chain formation and product research, development, and design. We further conclude that although both decision tree and matrix methods are formulated herein to reveal optimal life cycle scenarios, the use of demand matrices is preferred in the preparation of a formal mathematical construct. Further, for both methods, data collection and assessment are facilitated by the use of sub-inventories (or as a complete LCI within an LCI) for process alternatives and the full consideration of material alternatives to avoid the creation of artificial multi-functional processes. Recommendations and Perspectives  The methods described here are used in the assessment of forest management alternatives and are being further developed to form national commodity models considering technology alternatives, national production mixes and imports, and point-to-point transportation models. ESS-Submission Editor: Thomas Gloria, PhD (t.gloria@fivewinds.com)     

Life cycle assessment of silicon wafer processing for microelectronic chips and solar cells

Purpose  

The life cycle assessment of silicon wafer processing for microelectronic chips and solar cells aims to provide current and comprehensive data. In view of the very fast market developments, for solar cell fabrication the influence of technology and capacity variations on the overall environmental impact was also investigated and the data were compared with the widely used ecoinvent data.     

Effects of Distribution Choice on the Modeling of Life Cycle Inventory Uncertainty: An Assessment on the Ecoinvent v2.2 Database

Life cycle inventory data have multiple sources of uncertainty. These data uncertainties are often modeled using probability density functions, and in the ecoinvent database the lognormal distribution is used by default to model exchange uncertainty values. The aim of this article is to systematically measure the effect of this default distribution by changing from the lognormal to several other distribution functions and examining how this change affects the uncertainty of life cycle assessment results. Using the ecoinvent 2.2 inventory database, data uncertainty distributions are switched from the lognormal distribution to the normal, triangular, and gamma distributions. The effect of the distribution switching is assessed for both impact assessment results of individual products system, as well as comparisons between product systems. Impact assessment results are generated using 5,000 Monte Carlo iterations for each product system, using the Intergovernmental Panel on Climate Change (IPCC) 2001 (100‐year time frame) method. When comparing the lognormal distribution to the alternative default distributions, the difference in the resulting median and standard deviation values range from slight to significant, depending on the distributions used by default. However, the switch shows practically no effect on product system comparisons. Yet, impact assessment results are sensitive to how the data uncertainties are defined. In this article, we followed what we believe to be ecoinvent standard practice and preserved the “most representative” value. Practitioners should recognize that the most representative value can depart from the average of a probability distribution. Consistent default distribution choices are necessary when performing product system comparisons.