Eutrophication of aquatic ecosystems a new method for calculating the potential contributions of nitrogen and phosphorus

Aim, Scope and Background  

Aquatic eutrophication is a widespread problem in inland and coastal waters around the world and it should therefore be one of the impact categories to be considered in LCA studies of products and services. In LCAs there are several impact assessment methods to determine characterisation factors for eutrophying nutrients, but few methods have been developed to model fate and spatial aspects. One such method was developed as part of an LCA application of the Finnish forest industry. The aim of this study was to present this characterisation method in which the potential contributions of nitrogen and phosphorus to eutrophication of aquatic ecosystems are calculated. The use of the method was demonstrated by producing site/sector-specific characterisation factors and by constructing a reference value of aquatic eutrophication for Finland. A discussion of sensitivity and uncertainty aspects related to input data is also presented.     

How to obtain a precise and representative estimate for parameters in LCA

Background, Aim and Scope  Quite often there is need for precise and representative parameters in LCA studies. Probably the most relevant have direct influence on the functional unit, whose definition is crucial in the conduct of any LCA. Changes in the functional unit show directly in LCI and LCIA results. In comparative assertions, a bias in the functional unit may lead to a bias in the overall conclusions. Since quantitative data for the functional unit, such as geometric dimensions and specific weight, often vary, the question arises how to determine the functional unit, especially if a comparative assertion shall be representative for a region or market. Aim and scope of the study is to develop and apply methods for obtaining precise and representative estimates for the functional unit as one important parameter in an LCA study. Materials and Methods  Statistical sampling is applied in order to get empirical estimates for the weight of yoghurt cups, as a typical parameter for the functional unit. We used a two-stage sampling design, with stratified sampling in the first stage and three different sampling designs in the second stage, namely stratified, clustered, and a posteriori sampling. Sampling designs are motivated and described. In a case study, they are each used to determined a representative weight for 150 g yoghurt cups in Berlin, at the point of sale and within a specific time. In the first sampling stage, food markets are randomly selected, while in the second stage, yoghurt cups in these food markets are sampled. The sampling methods are applicable due to newly available internet data. These data sources and their shortcomings are described. Results  The random sampling procedure yields representative estimates, which are compared to figures for market leaders, i.e. yoghurt cups with very high occurrence in the supermarkets. While single types of yoghurt cups showed moderate uncertainty, representative estimates were highly precise. Discussion results show, for one, the performance of the applied statistical estimation procedures, and they show further that adding more information in the estimation procedure (on the shape of the cup, on the type of plastic, on the specific brand) helps reducing uncertainty. Conclusions  As conclusions, estimates and their uncertainty depend on the measurement procedure in a sensitive manner; any uncertainty information should be coupled with information on the measurement procedure, and it is recommended to use statistical sampling in order to reduce uncertainty for important parameters of an LCA study. Recommendations and Perspectives  Results for market leaders differed considerably from representative estimates. This implies to not use market leader data, or data with a high market share, as substitute for representative data in LCA studies. Statistical sampling has been barely used for Life Cycle Assessment. It turned out to be a feasible means for obtaining highly precise and representative estimates for the weight of yoghurt cups in the case study, based on empirical analysis. Further research is recommended in order to detect which parameters should best be investigated in LCA case studies; which data sources are available and recommended, and which sampling designs are appropriate for different application cases. ESS-Submission Editor: Seungdo Kim. PhD (kimseun@msu.edu)     

Evaluation of two simplified Life Cycle assessment methods

Goal, Scope and Background  Two methods of simplified LCA were evaluated and compared to the results of a quantitative LCA. These are the Environmentally responsible product assessment matrix developed by Graedel and Allenby and the MECO-method developed in Denmark. Methods  We used these in a case study and compared the results with the results from a quantitative LCA. The evaluation also included other criteria, such as the field of application and the level of arbitrariness. Results and Discussion  The MECO-method has some positive qualities compared to the Environmentally responsible product assessment matrix. Examples of this are that it generates information complementary to the quantitative LCA and provides the possibility to consider quantitative information when such is available. Some of the drawbacks with the Environmentally responsible product assessment matrix are that it does not include the whole lifecycle and that it allows some arbitrariness. Conclusions  Our study shows that a simplified and semi-quantitative LCA (such as the MECO-method) can provide information that is complementary to a quantitative LCA. In this case the method generates more information on toxic substances and other impacts, than the quantitative LCA. We suggest that a simplified LCA can be used both as a pre-study to a quantitative LCA and as a parallel assessment, which is used together with the quantitative LCA in the interpretation. Recommendations and Outlook  A general problem with qualitative analyses is how to compare different aspects. Life cycle assessments are comparative. The lack of a quantitative dimension hinders the comparison and can thereby hinder the usefulness of the qualitative method. There are different approaches suggested to semiquantify simplified methods in order to make quantitative comparisons possible. We think that the use of fabricated scoring systems should be avoided. If quantitative information is needed, one should consider performing a simplified quantitative LCA instead.     

Comparative lca of industrial objects part 1: lca data quality assurance — sensitivity analysis and pedigree matrix

Goal, Scope and Background  The main aim of this paper is to present some methodological considerations concerning existing methods used to assess quality of the LCA study. It relates mainly to the quality of data and the uncertainty of the LCA results. The first paper is strictly devoted to methodological aspects whereas, the second is presented in a separate article (Part II) and devoted mainly to a case study. Methods  The presented analysis is based on two well-known concepts: the Data Quality Indicators (DQIs) and the Pedigree Matrix. In the first phase, the Sensitivity Indicators are created on the basis of the sensitivity analysis and then linked with the DQIs and the Quality Classes. These parameters indicate the relative importance of input data and their theoretical quality levels. Next, the Weidema’s Pedigree Matrix (slightly modified) is used to establish the values of the new parameter called the Data Quality Distance (DQD) and to link them with the DQIs and Quality Classes. This way the information about the “real” quality levels is provided. Further analysis is performed using the probabilistic distributions and Monte Carlo simulations. Results and Discussion  Thanks to this approach it is possible to make a comparison between two types of the quality factors. On the one hand, the sensitivity analysis allows one to check the importance of input data and to determine their required quality. It is done according to the following relation: the higher the sensitivity indicator, the higher the importance of input data and the higher quality should be demanded. On the other hand the data have a certain real quality, not always in accord with the demanded one. To make possible a comparison between these two types of quality, it is necessary to find and develop a common denominator for them. Here, for this purpose the DQIs and Quality Classes are used. Conclusions  In the further stage of the assessment the DQIs are used to perform the uncertainty analysis of the LCA results. The results could be additionally analysed by using other techniques of interpretation: the sensitivity-, the contribution-, the comparative-, the discernability- and the uncertainty analysis. Recommendations and Outlook  The presented approach is put into practice to conduct the comparative LCA study for the industrial pumps by using the Ecoindicator99 method. Thanks to this, complex analysis of the credibility of the results is carried out. As a consequence, uncertainty ranges for the LCA results of every product system can be determined [1].     

Interpretation of comparative LCAs: external normalization and a method of mutual differences

Purpose

Identification of environmentally preferable alternatives in a comparative life cycle assessment (LCA) can be challenging in the presence of multiple incommensurate indicators. To make the problem more manageable, some LCA practitioners apply external normalization to find those indicators that contribute the most to their respective environmental impact categories. However, in some cases, these results can be entirely driven by the normalization reference, rather than the comparative performance of the alternatives. This study evaluates the influence of normalization methods on interpretation of comparative LCA to facilitate the use of LCA in decision-driven applications and inform LCA practitioners of latent systematic biases. An alternative method based on significance of mutual differences is proposed instead.

Methods

This paper performs a systematic evaluation of external normalization and describes an alternative called the overlap area approach for the purpose of identifying relevant issues in a comparative LCA. The overlap area approach utilizes the probability distributions of characterized results to assess significant differences. This study evaluates the effects in three LCIA methods, through application of four comparative studies. For each application, we call attention to the category indicators highlighted by each interpretation approach.

Results and discussion

External normalization in the three LCIA methods suffers from a systematic bias that emphasizes the same impact categories regardless of the application. Consequently, comparative LCA studies that employ external normalization to guide a selection may result in recommendations dominated entirely by the normalization reference and insensitive to data uncertainty. Conversely, evaluation of mutual differences via the overlap area calls attention to the impact categories with the most significant differences between alternatives. The overlap area approach does not show a systematic bias across LCA applications because it does not depend on external references and it is sensitive to changes in uncertainty. Thus, decisions based on the overlap area approach will draw attention to tradeoffs between alternatives, highlight the role of stakeholder weights, and generate assessments that are responsive to uncertainty.

Conclusions

The solution to the issues of external normalization in comparative LCAs proposed in this study call for an entirely different algorithm capable of evaluating mutual differences and integrating uncertainty in the results.

     

Quantifying system uncertainty of life cycle assessment based on Monte Carlo simulation

Background, aim, and scope

Many studies evaluate the results of applying different life cycle impact assessment (LCIA) methods to the same life cycle inventory (LCI) data and demonstrate that the assessment results would be different with different LICA methods used. Although the importance of uncertainty is recognized, most studies focus on individual stages of LCA, such as LCI and normalization and weighting stages of LCIA. However, an important question has not been answered in previous studies: Which part of the LCA processes will lead to the primary uncertainty? The understanding of the uncertainty contributions of each of the LCA components will facilitate the improvement of the credibility of LCA.

Methodology

A methodology is proposed to systematically analyze the uncertainties involved in the entire procedure of LCA. The Monte Carlo simulation is used to analyze the uncertainties associated with LCI, LCIA, and the normalization and weighting processes. Five LCIA methods are considered in this study, i.e., Eco-indicator 99, EDIP, EPS, IMPACT 2002+, and LIME. The uncertainty of the environmental performance for individual impact categories (e.g., global warming, ecotoxicity, acidification, eutrophication, photochemical smog, human health) is also calculated and compared. The LCA of municipal solid waste management strategies in Taiwan is used as a case study to illustrate the proposed methodology.

Results

The primary uncertainty source in the case study is the LCI stage under a given LCIA method. In comparison with various LCIA methods, EDIP has the highest uncertainty and Eco-indicator 99 the lowest uncertainty. Setting aside the uncertainty caused by LCI, the weighting step has higher uncertainty than the normalization step when Eco-indicator 99 is used. Comparing the uncertainty of various impact categories, the lowest is global warming, followed by eutrophication. Ecotoxicity, human health, and photochemical smog have higher uncertainty.

Discussion

In this case study of municipal waste management, it is confirmed that different LCIA methods would generate different assessment results. In other words, selection of LCIA methods is an important source of uncertainty. In this study, the impacts of human health, ecotoxicity, and photochemical smog can vary a lot when the uncertainties of LCI and LCIA procedures are considered. For the purpose of reducing the errors of impact estimation because of geographic differences, it is important to determine whether and which modifications of assessment of impact categories based on local conditions are necessary.

Conclusions

This study develops a methodology of systematically evaluating the uncertainties involved in the entire LCA procedure to identify the contributions of different assessment stages to the overall uncertainty. Which modifications of the assessment of impact categories are needed can be determined based on the comparison of uncertainty of impact categories.

Recommendations and perspectives

Such an assessment of the system uncertainty of LCA will facilitate the improvement of LCA. If the main source of uncertainty is the LCI stage, the researchers should focus on the data quality of the LCI data. If the primary source of uncertainty is the LCIA stage, direct application of LCIA to non-LCIA software developing nations should be avoided.     

Applying voting rules to panel-based decision making in LCA

Background, aim, and scope  Cross-category weighting is one possible way to facilitate internal decision making when dealing with ambiguous impact assessment results, with simple additive weighting being a commonly used method. Yet, the question as to whether the methods applied today can, in fact, identify the most “environmentally friendly” alternative from a group perspective remains unanswered. The aim of this paper is to propose a new method for group decision making that ensures the effective identification of the most preferable alternative. Materials and methods  Common approaches to deduce a single set of weighting factors for application in a group decision situation (e.g., arithmetic mean, consensus) are discussed based on simple mathematics, empirical data, and thought experiments. After proposing an extended definition for “effectiveness” in group decision making, the paper recommends the use of social choice theory whose main focus is to identify the most preferable alternative based on individuals’ rankings of alternatives. The procedure is further supplemented by a Monte Carlo analysis to facilitate the assessment of the result’s robustness. Results  The general feasibility of the method is demonstrated. It generates a complete ranking of alternatives, which does not contain cardinal single scores. In terms of effectiveness, the mathematical structure of the procedure ensures the eligibility for compromise of the group decision proposal. The sensitivity analysis supports the decision makers in understanding the robustness of the proposed group ranking. Discussion  The method is based upon an extended definition of effectiveness which acknowledges the eligibility for compromise as the core requirement in group decision contexts. It is shown that multi-attribute decision-making (MADM) methods in use in life cycle assessment (LCA) today do not necessarily meet this requirement because of their mathematical structure. Further research should focus on empirical proof that the generated group results are indeed more eligible for compromise than results generated by current methods that utilize an averaged group weighting set. This is closely related to the question considering under which mathematical constraints it is even possible to generate an essentially different result. Conclusions  The paper describes a new multi-attribute group decision support system (MGDSS) for the identification of the most preferable alternative(s) for use in panel-based LCA studies. The main novelty is that it refrains from deducing a single set of weighting factors which is supposed to represent the panel as a whole. Instead, it applies voting rules that stem from social choice theory. Because of its mathematical structure, the procedure is deemed superior to common approaches in terms of its effectiveness. Recommendations and perspectives  The described method may be recommended for use in internal, panel-based LCA studies. In addition, the basic approach of the method—the combination of MADM methods with social choice theory—can be recommended for use in all those situations where multi-attribute decisions are to be made in a group context.     

Assessing potential desertification environmental impact in life cycle assessment

Background, aim and scope  

Life cycle assessment (LCA) enables the objective assessment of global environmental burdens associated with the life cycle of a product or a production system. One of the main weaknesses of LCA is that, as yet, there is no scientific agreement on the assessment methods for land-use related impacts, which results in either the exclusion or the lack of assessment of local environmental impacts related to land use. The inclusion of the desertification impact in LCA studies of any human activity can be important in high-desertification risk regions.     

Comparative LCA of industrial objects

Goal, Scope and Background  This paper is the second part of the publication which is devoted to comparative LCA analysis of the industrial pumps. The previous paper deals with the methodological aspects concerning quality assessment and forms an independent work. This paper uses practically only the methodological suggestions made there. The main aim of the presented study is to make a comparison between the industrial pumps which are based on two different technologies. The Life Cycle Assessment method is used to check whether the differences of the manufacturing processes influence the level of the potential environmental impact during the whole life cycle of the analysed products. Methods  The Life Cycle Assessment is carried out using the Ecoindicator99 method. Additionally, an extensive quality analysis of the LCA study is made (Part I). To make the process of an identification of the data easier and faster, they are assigned to a special data documentation form. To ensure the credibility of the LCA results different methods of interpretation are used. Results and Discussion  The LCA analysis shows clear superiority of the pumps manufactured using modern technology. It seems that this superiority results not only from the differences in the emissions, but also from different characteristics of effectiveness in the usage stage. Thanks to the uncertainty analysis, each LCA result is provided with the range of uncertainty. Conclusions  The LCA results are supported by different techniques of interpretation: the sensitivity-, the contribution-, the comparative-, the discernability- and the uncertainty analysis. There is strong evidence of the superiority of the pumps based on the modern technology. Recommendations and Outlook  The main source of the environmental impact in the case of pumps is the usage stage and the consumption of energy. That is why it should be the main area to improve. The LCA results show that actions taken in the usage stage and energy consumption can lead to a considerable reduction of the environmental impacts.     

Integration of life cycle assessment in the environmental information system

Background, aim, and scope  As the sustainability improvement becomes an essential business task of industry, a number of companies are adopting IT-based environmental information systems (EIS). Life cycle assessment (LCA), a tool to improve environmental friendliness of a product, can also be systemized as a part of the EIS. This paper presents a case of an environmental information system which is integrated with online LCA tool to produce sets of hybrid life cycle inventory and examine its usefulness in the field application of the environmental management. Main features  Samsung SDI Ltd., the producer of display panels, has launched an EIS called Sustainability Management Initiative System (SMIS). The system comprised modules of functions such as environmental management system (EMS), green procurement (GP), customer relation (e-VOC), eco-design, and LCA. The LCA module adopted the hybrid LCA methodology in the sense that it combines process LCA for the site processes and input–output (IO) LCA for upstream processes to produce cradle-to-gate LCA results. LCA results from the module are compared with results of other LCA studies made by the application of different methodologies. The advantages and application of the LCA system are also discussed in light of the electronics industry. Results and discussion  LCA can play a vital role in sustainability management by finding environmental burden of products in their life cycle. It is especially true in the case of the electronics industry, since the electronic products have some critical public concerns in the use and end-of-life phase. SMIS shows a method for hybrid LCA through online data communication with EMS and GP module. The integration of IT-based hybrid LCA in environmental information system was set to begin in January 2006. The advantage of the comparing and regular monitoring of the LCA value is that it improves the system completeness and increases the reliability of LCA. By comparing the hybrid LCA and process LCA in the cradle-to-gate stage, the gap between both methods of the 42-in. standard definition plasma display panel (PDP) ranges from 1% (acidification impact category) to −282% (abiotic resource depletion impact category), with an average gap of 68.63%. The gaps of the impact categories of acidification (AP), eutrophication (EP), and global warming (GWP) are relatively low (less than 10%). In the result of the comparative analysis, the strength of correlation of three impact categories (AP, EP, GWP) shows that it is reliable to use the hybrid LCA when assessing the environmental impacts of the PDP module. Hybrid LCA has its own risk on data accuracy. However, the risk is affordable when it comes to the comparative LCA among different models of similar product line of a company. In the results of 2 years of monitoring of 42-in. Standard definition PDP, the hybrid LCA score has been decreased by 30%. The system also efficiently shortens man-days for LCA study per product. This fact can facilitate the eco-design of the products and can give quick response to the customer's inquiry on the product's eco-profile. Even though there is the necessity for improvement of process data currently available, the hybrid LCA provides insight into the assessments of the eco-efficiency of the manufacturing process and the environmental impacts of a product. Conclusions and recommendations  As the environmental concerns of the industries increase, the need for environmental data management also increases. LCA shall be a core part of the environmental information system by which the environmental performances of products can be controlled. Hybrid type of LCA is effective in controlling the usual eco-profile of the products in a company. For an industry, in particular electronics, which imports a broad band of raw material and parts, hybrid LCA is more practicable than the classic LCA. Continuous efforts are needed to align input data and keep conformity, which reduces data uncertainty of the system.     

Gone…but not away—addressing the problem of long-term impacts from landfills in LCA

Background, aim and scope  Land filling of materials with content of toxic metals or highly persistent organic compounds has posed a problem for life cycle assessment (LCA) practitioners for many years. The slow release from the landfill entails a dilution in time, which is dramatic compared to other emissions occurring in the life cycle, and with its focus on the emitted mass, LCA is poorly equipped to handle this difference. As a consequence, the long-term emissions from landfills occurring over thousands of years are often disregarded, which is unacceptable to many stakeholders considering the quantities of toxic substances that can be present. On the other hand, inclusion of all future emissions (over thousands of years) in the inventories potentially dominates all other impacts from the product system. The paper aims to present a pragmatic approach to address this dilemma. Materials and methods  Two new impact categories are introduced representing the stored ecotoxicity and stored human toxicity of the contaminants remaining in the landfill after a ‘foreseeable’ time period of 100 years. The impact scores are calculated using the normal characterisation factors for the ecotoxicity and human toxicity impact categories, and they represent the toxicity potentials of what remains in the landfill after 100 years (hence the term ‘stored’ (eco)toxicity). Normalisation references are developed for the stored toxicity categories based on Danish figures to support comparison with indicator scores for the conventional environmental impact categories. In contrast to the scores for the conventional impact categories, it is uncertain to what extent the stored toxicity scores represent emissions, which will occur at all. Guidance is given on how to reflect this uncertainty in the weighting and interpretation of the scores. Results and discussion  In landfills and road constructions used to deposit residuals from incinerators, less than 1% of the content of metals is leached within the first 100 years. The stored toxicity scores are therefore much higher than the conventional impact scores that represent the actual emissions. Several examples are given illustrating the use and potential significance of the stored toxicity categories. Conclusions and perspectives  The methodology to calculate stored human and ecotoxicity is a simple and pragmatic approach to address LCA’s problem of treating the slow long-term emissions at very low concentrations appropriately. The problem resides in the inventory analysis and the impact assessment, and the methodology circumvents the problem by converting it into a weighting and interpretation issue accommodating the value-based discussion of how to weight potential effects in the far future. Electronic supplementary material  The online version of this article (doi:) contains supplementary material, which is available to authorized users.

Pesticide ecotoxicological effect factors and their uncertainties for freshwater ecosystems

Background, aim, and scope  Characterization factors for ecotoxicity in the Life Cycle Impact Assessment (LCIA) are used to convert emissions into ecotoxicological impacts. Deriving them involves a fate and an effect analysis step. The fate factor quantifies the change in environmental concentration per unit of emission, while the effect factor quantifies the change in impact on the ecosystem per unit of environmental concentration. This paper calculates freshwater ecotoxicological effect factors for 397 pesticides belonging to 11 pesticide-specific toxic modes of action (TMoA), such as acetylcholinesterase inhibition and photosynthesis inhibition. Moreover, uncertainties in the effect factors due to uncertain background concentrations and due to limited toxicity data are quantified. Methods  To calculate median ecotoxicological effect factors (EEFs), toxic pressure assessments were made, based on the species sensitivity distribution—and the multisubstance potentially affected fraction—concept. The EEF quantifies an estimate of the fraction of species that is probably affected due to a marginal change in concentration of a pesticide. EEFs were divided into a TMoA-specific and a chemical-specific part, which were calculated on the basis of physicochemical properties, emissions, and toxicity data. Propagation of parameter uncertainty in the EEFs and the TMoA- and chemical-specific parts was quantified by Monte Carlo simulation and results were reported as 90% confidence intervals. Results  Median EEFs range from 2·10⁻³ to 7·10⁶ l/g. Uncertainty in the TMoA-specific part is dominated by uncertainty in the TMoA-specific spread in species sensitivity and by uncertainty in the effective toxicity of a TMoA. Uncertainty in the chemical-specific part of the EEFs depends on the number of species for which toxicity data are available to calculate average toxicity (n _s) and ranges from a median uncertainty of 2.6 orders of magnitude for n _s = 2 to one order of magnitude for n _s ≥ 4. The TMoA-specific effect factor for systemic fungicides shows the largest uncertainty range. For seven TMoAs, uncertainty ranges of the TMoA-specific effect factor are less than two orders of magnitude. For the other four TMoAs, the EEF uncertainty range is between two and eight orders of magnitude. For the chemical-specific part of the EEFs, we found that variation in uncertainty readily decreases for pesticides for which toxicity data are available for at least three species. Discussion  The same parameters that contributed most to uncertainty were found for pesticides as were found before for high-production-volume chemicals. However, uncertainty in concentrations of pesticides was lower. TMoA-specific factors obtained with the applied nonlinear method differ up to nine orders of magnitude from the factor of 0.5, which is used in the linear method. With the applied method, a distinction in EEFs can be made among different TMoAs. Conclusions   Ecotoxicological effect factors are presented, including overviews of their uncertainty ranges and the main contributors to uncertainty. The applied nonlinear method provides the possibility to quantify parameter uncertainty in the TMoA-specific part of the ecotoxicological effect factor, which is helpful to get more insight in how uncertainty in ecotoxicological characterization factors can be reduced. Recommendations and perspectives  The calculated uncertainty ranges can be included in life cycle assessment (LCA) case studies, which allows for better interpretation of LCA results obtained with the EEFs. To put the uncertainty in effect factors into perspective within LCIA, more information on the uncertainty in fate factors should be derived. Electronic supplementary material  The online version of this article (doi:) contains supplementary material, which is available to authorized users.     

Parameter uncertainty in LCA: stochastic sampling under correlation

Purpose  

At the parameter level, data inaccuracy, data gaps, and the use of unrepresentative data have been recognized as sources of uncertainty in life cycle assessment (LCA). In many LCA uncertainty studies, parameter distributions were created based on the measured variability or on “rules of thumb,” but the possible existence of correlation was not explored. The correlation between parameters may alter the sampling space and, thus, yield unrepresentative results. The objective of this article is to describe the effect of correlation between input parameters (and the final product) on the outcome of an uncertainty analysis, carried out for an LCA of an agricultural product.     

Categorizing water for LCA inventory

Purpose  

As impact assessment methods for water use in LCA evolve, so must inventory methods. Water categories that consider water quality must be defined within life cycle inventory. The method presented here aims to establish water categories by source, quality parameter and user.     

Uncertainties in the application of the species area relationship for characterisation factors of land occupation in life cycle assessment

Purpose  

Uncertainties in land use damage modeling are recognized, but hardly quantified in life cycle assessment (LCA). The objective of this study is to analyze the influence of various key assumptions and uncertainties within the development of characterisation factors (CFs) for land use in LCA. We assessed the influence on land use CFs of (1) parameter uncertainty and (2) the choice for a constant or land use-specific species accumulation factor z and including or excluding regional effects.     

Regional LCA in a global perspective. A basis for spatially differentiated environmental life cycle assessment

Background, aim and scope  

In the context of environmental life cycle assessment (LCA), life cycle impact assessment (LCIA) is one of the central issues with respect to modelling and methodological data collection. The thesis described in this paper focusses on the assessment of toxicity-related impacts, and on the collection of normalisation data. A view on the complementary roles of LCA toxicity assessment on the one hand and human and environmental risk assessment (HERA) on the other is presented, and the global, spatially differentiated LCA toxicity assessment model GLOBOX for the assessment of organics and metals is described. Normalisation factors for the year 2000 are calculated on a global as well as on a European level.     

Uncertainty calculation in life cycle assessments

Goal and Background  Uncertainty is commonly not taken into account in LCA studies, which downgrades their usability for decision support. One often stated reason is a lack of method. The aim of this paper is to develop a method for calculating the uncertainty propagation in LCAs in a fast and reliable manner. Approach  The method is developed in a model that reflects the calculation of an LCA. For calculating the uncertainty, the model combines approximation formulas and Monte Carlo Simulation. It is based on virtual data that distinguishes true values and random errors or uncertainty, and that hence allows one to compare the performance of error propagation formulas and simulation results. The model is developed for a linear chain of processes, but extensions for covering also branched and looped product systems are made and described. Results  The paper proposes a combined use of approximation formulas and Monte Carlo simulation for calculating uncertainty in LCAs, developed primarily for the sequential approach. During the calculation, a parameter observation controls the performance of the approximation formulas. Quantitative threshold values are given in the paper. The combination thus transcends drawbacks of simulation and approximation. Conclusions and Outlook  The uncertainty question is a true jigsaw puzzle for LCAs and the method presented in this paper may serve as one piece in solving it. It may thus foster a sound use of uncertainty assessment in LCAs. Analysing a proper management of the input uncertainty, taking into account suitable sampling and estimation techniques; using the approach for real case studies, implementing it in LCA software for automatically applying the proposed combined uncertainty model and, on the other hand, investigating about how people do decide, and should decide, when their decision relies on explicitly uncertain LCA outcomes-these all are neighbouring puzzle pieces inviting to further work.     

Analytical Propagation of Uncertainty in Life Cycle Assessment Using Matrix Formulation

Inventory data and characterization factors in life cycle assessment (LCA) contain considerable uncertainty. The most common method of parameter uncertainty propagation to the impact scores is Monte Carlo simulation, which remains a resource‐intensive option—probably one of the reasons why uncertainty assessment is not a regular step in LCA. An analytical approach based on Taylor series expansion constitutes an effective means to overcome the drawbacks of the Monte Carlo method. This project aimed to test the approach on a real case study, and the resulting analytical uncertainty was compared with Monte Carlo results. The sensitivity and contribution of input parameters to output uncertainty were also analytically calculated. This article outlines an uncertainty analysis of the comparison between two case study scenarios. We conclude that the analytical method provides a good approximation of the output uncertainty. Moreover, the sensitivity analysis reveals that the uncertainty of the most sensitive input parameters was not initially considered in the case study. The uncertainty analysis of the comparison of two scenarios is a useful means of highlighting the effects of correlation on uncertainty calculation. This article shows the importance of the analytical method in uncertainty calculation, which could lead to a more complete uncertainty analysis in LCA practice.     

A characterization model with spatial and temporal resolution for life cycle impact assessment of photochemical precursors in the United States

Background, aim, and scope  Traditional life cycle impact assessment methodologies have used aggregated characterization factors, neglecting spatial and temporal variations in regional impacts like photochemical oxidant formation. This increases the uncertainty of the LCA results and diminishes the ease of decision-making. This study compares four common impact assessment methods, CML2001, Eco-indicator 99, TRACI, and EDIP2003, on their underlying models, spatial and temporal resolution, and the level at which photochemical oxidant impacts are calculated. A new characterization model is proposed that incorporates spatial and temporal differentiation. Materials and methods  A photochemical air quality modeling system (CAMx-MM5-SMOKE) is used to simulate the process of formation, transformation, transport, and removal of photochemical pollutants. Monthly characterization factors for individual US states are calculated at three levels along the cause–effect chain, namely, fate level, human and ecosystem exposure level, and human effect level. Results and discussion  The results indicate that a spatial variability of one order of magnitude and a temporal variability of two orders of magnitude exist in both the fate level and human exposure and effect level characterization factors for NO_x. The summer time characterization factors for NO_x are higher than the winter time factors. However, for anthropogenic VOC, the summer time factors are lower than the winter time in almost half of the states. This is due to the higher emission rates of biogenic VOCs in the summer. The ecosystem exposure factors for NO_x and VOC do not follow a regular pattern and show a spatial variation of about three orders of magnitude. They do not show strong correlation with the human exposure factors. Sensitivity analysis has shown that the effect of meteorology and emission inputs is limited to a factor of three, which is several times smaller than the variation seen in the factors. Conclusions  Uncertainties are introduced in the characterization of photochemical precursors due to a failure to consider the spatial and temporal variations. Seasonal variations in photochemical activity influence the characterization factors more than the location of emissions. The human and ecosystem exposures occur through different mechanisms, and impacts calculated at the fate level based only on ozone concentration are not a good indicator for ecosystem impacts. Recommendations and perspectives  Spatial and temporal differentiation account for fate and transport of the pollutant, and the exposure of and effect on the sensitive human population or ecosystem. Adequate resolution for seasonal and regional processes, like photochemical oxidant formation, is important to reduce the uncertainty in impact assessment and improve decision-making power. An emphasis on incorporating some form of spatial and temporal information within standard LCI databases and using adequately resolved characterization factors will greatly increase the fidelity of a standard LCA. Electronic supplementary material  The online version of this article (doi:) contains supplementary material, which is available to authorized users.     

Uncertainty of global warming potential for milk production on a New Zealand farm and implications for decision making

Background, aim and scope  

As a food exporting nation, New Zealand recognises that the Global Warming Potential (GWP) impact of agriculture has become important to food customers. Food production policy and industry analysts make GWP decisions based on greenhouse gas inventory and life cycle assessment (LCA) results. For decision making, the level of confidence associated with information is important. However, treatment of uncertainty has been problematic in LCA, especially in agricultural systems. In this paper, the GWP of 1 kg of milk was used as a case study to test the feasibility of quantifying uncertainties by Monte Carlo simulation in an LCA applied to an agriculture product. The study also contributes to the development of good practice and has implications for the incorporation of uncertainties into decision making.