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.

     

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.

     

Uncertainty analysis in LCA using precalculated aggregated datasets

Purpose

Some LCA software tools use precalculated aggregated datasets because they make LCA calculations much quicker. However, these datasets pose problems for uncertainty analysis. Even when aggregated dataset parameters are expressed as probability distributions, each dataset is sampled independently. This paper explores why independent sampling is incorrect and proposes two techniques to account for dependence in uncertainty analysis. The first is based on an analytical approach, while the other uses precalculated results sampled dependently.

Methods

The algorithm for generating arrays of dependently presampled aggregated inventories and their LCA scores is described. These arrays are used to calculate the correlation across all pairs of aggregated datasets in two ecoinvent LCI databases (2.2, 3.3 cutoff). The arrays are also used in the dependently presampled approach. The uncertainty of LCA results is calculated under different assumptions and using four different techniques and compared for two case studies: a simple water bottle LCA and an LCA of burger recipes.

Results and discussion

The meta-analysis of two LCI databases shows that there is no single correct approximation of correlation between aggregated datasets. The case studies show that the uncertainty of single-product LCA using aggregated datasets is usually underestimated when the correlation across datasets is ignored and that the magnitude of the underestimation is dependent on the system being analysed and the LCIA method chosen. Comparative LCA results show that independent sampling of aggregated datasets drastically overestimates the uncertainty of comparative metrics. The approach based on dependently presampled results yields results functionally identical to those obtained by Monte Carlo analysis using unit process datasets with a negligible computation time.

Conclusions

Independent sampling should not be used for comparative LCA. Moreover, the use of a one-size-fits-all correction factor to correct the calculated variability under independent sampling, as proposed elsewhere, is generally inadequate. The proposed approximate analytical approach is useful to estimate the importance of the covariance of aggregated datasets but not for comparative LCA. The approach based on dependently presampled results provides quick and correct results and has been implemented in EcodEX, a streamlined LCA software used by Nestlé. Dependently presampled results can be used for streamlined LCA software tools. Both presampling and analytical solutions require a preliminary one-time calculation of dependent samples for all aggregated datasets, which could be centrally done by database providers. The dependent presampling approach can be applied to other aspects of the LCA calculation chain.

     

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.

     

Modelling Representative Life Cycle Inventories for Swiss Arable Crops (9 pp)

Goal, Scope and Background Life cycle inventories (LCIs) of agricultural products, infrastructure, inputs and processes are required to optimise food supply chains. In the past, the use of LCA was hindered by the limited availability of databases with LCIs for such agricultural inputs, processes and products in combination with LCIs of other major economic sectors. The ecoinvent database covers this need for the Swiss, and to an extent, the European context. A suitable approach had to be outlined for defining representative datasets for products from arable crops, since there was no comprehensive survey of agricultural production.Methods No single data source was available for defining representative datasets for arable crops. It was therefore decided to define model crops on the basis of a variety of sources in collaboration with experts on the crops in question. The datasets were validated by experts and by comparison with literature. Field emissions were calculated using a set of models taking into account situation-specific parameters. Data defined by this procedure are more generally usable, but their definition is also more laborious. Results and Discussion Selected results (inventories and impact assessment) are presented for infrastructure (buildings, machinery), work processes, fertilisers, pesticides, seed and arable crop products. Infrastructure has a higher share of environmental impacts than in typical industrial processes, often due to low utilisation rates. Energy use is dominated by mechanisation, the use of mineral fertilisers (particularly nitrogen) and grain drying. Eutrophication is caused mainly by nitrogen compounds. In general, field emissions are of decisive importance for many environmental impacts. Conclusion and Outlook The ecoinvent database provides representative agricultural data for the Swiss, and to an extent, the European context. It also provides the meta-information necessary for deciding whether a dataset is suitable for the purpose of a particular LCA study. To further improve the representativeness of the datasets, an environmental farm monitoring network is required.     

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.

     

Enhancing Life-Cycle Inventories via Reconciliation with the Laws of Thermodynamics

Abstract: Obtaining reliable results from life-cycle assessment studies is often quite difficult because life-cycle inventory (LCI) data are usually erroneous, incomplete, and even physically meaningless. The real data must satisfy the laws of thermodynamics, so the quality of LCI data may be enhanced by adjusting them to satisfy these laws. This is not a new idea, but a formal thermodynamically sound and statistically rigorous approach for accomplishing this task is not yet available. This article proposes such an approach based on methods for data rectification developed in process systems engineering. This approach exploits redundancy in the available data and models and solves a constrained optimization problem to remove random errors and estimate some missing values. The quality of the results and presence of gross errors are determined by statistical tests on the constraints and measurements. The accuracy of the rectified data is strongly dependent on the accuracy and completeness of the available models, which should capture information such as the life-cycle network, stream compositions, and reactions. Such models are often not provided in LCI databases, so the proposed approach tackles many new challenges that are not encountered in process data rectification. An iterative approach is developed that relies on increasingly detailed information about the life-cycle processes from the user. A comprehensive application of the method to the chlor-alkali inventory being compiled by the National Renewable Energy Laboratory demonstrates the benefits and challenges of this approach.     

Applying Leontief's Price Model to Estimate Missing Elements in Hybrid Life Cycle Inventories

This article presents an approach to estimate missing elements in hybrid life cycle inventories. Its development is motivated by a desire to rationalize inventory compilation while maintaining the quality of the data. The approach builds on a hybrid framework, that is, a combination of process‐ and input–output‐based life cycle assessment (LCA) methodology. The application of Leontief's price model is central in the proposed procedure. Through the application of this approach, an inventory with no cutoff with respect to costs can be obtained. The formal framework is presented and discussed. A numerical example is provided in Supplementary Appendix S1 on the Web.     

German network on life cycle inventory data

Reliability of Life Cycle Assessment (LCA) results depends on the availability and quality of Life Cycle Inventory (LCI) data. In order to provide high-quality LCI data for background systems in LCA and to make it applicable to a wider range of fields, harmonization strategies for already existing datasets and databases are required. In view of the high significance of LCI data as a basis of major fields of action within a sustainability strategy, the German Helmholtz Association (HGF), under the leadership of the Forschungszentrum Karlsruhe (FZK) has taken up this issue in its research programme. In 2002, the FZK conducted a preliminary study on ‘Quality Assurance and User-oriented Supply of a Life Cycle Inventory Data’ funded by the Federal Ministry of Education and Research (BMBF). Within the framework of this study, a long-term concept for improving the scientific fundamentals and practical use of LCI data was developed in association with external experts. The focus is on establishing a permanent German ‘Network on Life Cycle Inventory Data’ which will serve as the German information and cooperation platform for all scientific and non-scientific actors in the field of life cycle analysis. This network will integrate expertise on LCA in Germany, harmonise methodology and data, and use the comprehensive expert panel as an efficient basis for further scientific development and practical use of LCA. At the same time, this network will serve as a platform for cooperation on an international level. Current developments address methodological definitions for the initial information infrastructure. As a novel element, user needs are differentiated in parallel according to the broad application fields of LCI-data from product declaration to process design. Case studies will be used to define tailored interfaces for the database, since different data quality levels will be encountered.     

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.     

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.     

Manufacturing and Disposal of Building Materials and Inventorying Infrastructure in ecoinvent (8 pp)

Goal, Scope and Background The present paper describes the goal and scope of building material inventories in the ecoinvent database and gives an overview of its content. The ecoinvent database provides generic life cycle inventories for building material production and related processing. They can be used as background data for different LCA applications. Their geographical and temporal scope is Switzerland or Europe and the year 2000.Methods Data is inventoried as unit processes. Consistency throughout different sources is heeded by systematically estimating missing data. Infrastructure is consequently considered. Different disposal options are modelled.Results and Conclusion The ecoinvent data provide a harmonised basis for different kinds of building materials. Even though not all datasets could be established on the same quality level, the results generally are believed to be comparable. Since data are generic, they are, however, not suitable to directly compare specific products. Disposal is relevant for the environmental burdens of uses of building materials. Complete life cycles have to be assessed. For this purpose, cumulative energy demand (CED) is not a suitable indicator.Recommendation and Perspective In future versions of ecoinvent, data quality could be further improved. The database should be extended to include further building materials from secondary materials. To do so, the methodological treatment of secondary materials needs special attention.     

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 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.     

Developments in Wood and Packaging Materials Life Cycle Inventories in ecoinvent (9 pp)

Goal, Scope and Background This paper gives an overview on how the wood and packaging material production is inventoried in ecoinvent. Packaging materials have been a very important topic in the area of Life Cycle Assessment for more than twenty years. Wood is the most important renewable material and regenerative fuel used worldwide, and an important raw material for paper / board. Several methodological problems arising when inventorying wood for material and energetic uses in a generic database are discussed in more detail. Within the ecoinvent project, the Swiss data base for life cycle inventory data, two reports are dedicated to these two important topics – report No. 9 for wood and report No. 11 for packaging materials. Methods The whole wood chain has been modeled in a consistent way. This allows one to use this data for LCAs of building materials, bioenergy or paper production. The data represent average technologies used in Central Europe in the year 2000. A revenue-based co-product allocation approach is used for the different outputs. Correction factors are introduced for the consistent modeling of mass-based, material inherent wood properties such as solar energy, carbon uptake and land use. For packaging materials, the datasets represent European average data for the most often used materials as well as specific datasets for the production of actual packaging boxes and containers.Results and Discussion For wood, revenue-based allocation and the use of the correction factors for mass-related wood properties are shown and explained. For packaging materials, the importance of the raw material wood to the total load is shown. Furthermore trends in the data inventories for board packaging materials over the last two decades are discussed: mainly due to the increased comprehensiveness of the data, higher cumulative emissions can be observed. Conclusion For wood, the database ecoinvent provides consistent datasets for the entire chain from forestry to intermediate products such as timber, different types of wood-based boards, chips, pellets, etc. For packaging materials, the number of datasets of basic materials has been extended. A modular concept for actual packaging container datasets allows the user an easy modeling of various types of packaging containers/boxes. In the area of paper and board, a comprehensive database for the production of various types of pulp, paper and board is provided, which is representative for the average European production situation. Outlook Since wood is only limited and representative data for Europe is therefore not included, an update in the near future would be reasonable. Possible further extensions in the future could include various, final wooden products. For the data on paper/board, different levels of quality are observed, requiring a selective up-date of these data. Future extensions could include datasets for the import of pulp from overseas – especially from South America and Canada.     

Critical analysis of life cycle inventory datasets for organic crop production systems

Purpose

Organic agriculture (OA) has gained widespread popularity due to its view as a more sustainable method of farming. Yet OA and conventional agriculture (CA) can be found to have similar or varying environmental performance using tools such as life cycle assessment (LCA). However, the current state of LCA does not accurately reflect the effects of OA; thus the aim of the present study was to identify gaps in the inventory stage and suggest improvements.

Methods

This article presents for the first time a critical analysis of the life cycle inventory (LCI) of state-of-the-art organic crop LCIs from current and recommended LCA databases ecoinvent and AGRIBALYSE®. The effects of these limitations on LCA results were analyzed and detailed ways to improve upon them were proposed.

Results and discussion

Through this analysis, unrepresentative plant protection product (PPP) manufacturing and organic fertilizer treatment inventories were found to be the main limitations in background processes, due to either the lack of available usage statistics, exclusion from the study, or use of unrepresentative proxies. Many organic crop LCIs used synthetic pesticide or mineral fertilizer proxies, which may indirectly contain OA prohibited chemicals. The effect of using these proxies can contribute between 4–78% to resource and energy-related impact categories. In a foreground analysis, the fertilizer and PPP emission models utilized by ecoinvent and AGRIBALYSE® were not well adapted to organic-authorized inputs and used simplified modeling assumptions. These critical aspects can be transferred to respective LCAs that use this data, potentially yielding unrepresentative results for relevant categories. To improve accuracy and to contribute novel data to the scientific community, new manufacturing LCIs were created for a few of the missing PPPs, as well as recommendations for fertilizer treatment LCIs and more precise emission models for PPPs and fertilizers.

Conclusions

The findings in the present article add much needed transparency regarding the limitations of available OA LCIs, offers guidance on how to make OA LCIs more representative, allow for more accurate comparisons between conventional and OA, and help practitioners to better adapt LCA methodology to OA systems.

     

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.