Screening stochastic Life Cycle assessment inventory models

A screening methodology is presented that utilizes the linear structure within the deterministic life cycle inventory (LCI) model. The methodology ranks each input data element based upon the amount it contributes toward the final output. The identified data elements along with their position in the deterministic model are then sorted into descending order based upon their individual contributions. This enables practitioners and model users to identify those input data elements that contribute the most in the inventory stage. Percentages of the top ranked data elements are then selected, and their corresponding data quality index (DQI) value is upgraded in the stochastic LCI model. Monte Carlo computer simulations are obtained and used to compare the output variance of the original stochastic model with modified stochastic model. The methodology is applied to four real-world beverage delivery system LCA inventory models for verification. This research assists LCA practitioners by streamlining the conversion process when converting a deterministic LCI model to a stochastic model form. Model users and decision-makers can benefit from the reduction in output variance and the increase in ability to discriminate between product system alternatives.     

Data Quality

A methodology is presented to develop and analyze vectors of data quality attribute scores. Each data quality vector component represents the quality of the data element for a specific attribute (e.g., age of data). Several methods for aggregating the components of data quality vectors to derive one data quality indicator (DQI) that represents the total quality associated with the input data element are presented with illustrative examples. The methods are compared and it is proven that the measure of central tendency, or arithmetic average, of the data quality vector components as a percentage of the total quality range attainable is an equivalent measure for the aggregate DQI. In addition, the methodology is applied and compared to realworld LCA data pedigree matrices. Finally, a method for aggregating weighted data quality vector attributes is developed and an illustrative example is presented. This methodology provides LCA practitioners with an approach to increase the precision of input data uncertainty assessments by selecting any number of data quality attributes with which to score the LCA inventory model input data. The resultant vector of data quality attributes can then be analyzed to develop one aggregate DQI for each input data element for use in stochastic LCA modeling.     

Life cycle assessment capacity roadmap (section 1): decision-making support using LCA

When life cycle assessment (LCA) results do not show a clear and certain environmental preference of one choice over one or several alternatives, current methods are limited in their ability to inform decision-makers. To address this and related cross-cutting issues, a group of LCA practitioners has been working on a roadmap for capacity development in LCA. The roadmap is identifying common needs for development in LCA, which can then be addressed by the broader LCA community. The roadmap document on decision-making support, having undergone a public comment period, outlines the current state as well as needs and milestones to ensure progress continues apace. The roadmap document, available for download, covers five main areas of development: (1) performance measures of confidence, which identify the acceptable uncertainty for study results, while minimizing expenditures; (2) selection of impact categories, an area with multiple existing methods. The roadmap suggests codifying these methods and identifying their suitability to various applications; (3) normalization; while several methods of normalization are in use, the method with the greatest acceptance in the LCA community (i.e., relying on total or per capita regional emissions/extractions) has a number of methodological drawbacks; (4) weighting, which is a form of multi-criteria decision analysis (MCDA). The broader MCDA field can enrich LCA by providing studied methods of assessing trade-offs; and (5) visualization of results. Many other LCA capacity needs would benefit from documentation. These include but are not limited to the following: addressing ill-characterized uncertainty, life cycle inventory data needs, data format needs, and tool capabilities. Other roadmapping groups are forming and are looking for practitioners to support the effort.     

Model uncertainty analysis using data analytics for life-cycle assessment (LCA) applications

Purpose

Objective uncertainty quantification (UQ) of a product life-cycle assessment (LCA) is a critical step for decision-making. Environmental impacts can be measured directly or by using models. Underlying mathematical functions describe a model that approximate the environmental impacts during various LCA stages. In this study, three possible uncertainty sources of a mathematical model, i.e., input variability, model parameter (differentiate from input in this study), and model-form uncertainties, were investigated. A simple and easy to implement method is proposed to quantify each source.

Methods

Various data analytics methods were used to conduct a thorough model uncertainty analysis; (1) Interval analysis was used for input uncertainty quantification. A direct sampling using Monte Carlo (MC) simulation was used for interval analysis, and results were compared to that of indirect nonlinear optimization as an alternative approach. A machine learning surrogate model was developed to perform direct MC sampling as well as indirect nonlinear optimization. (2) A Bayesian inference was adopted to quantify parameter uncertainty. (3) A recently introduced model correction method based on orthogonal polynomial basis functions was used to evaluate the model-form uncertainty. The methods are applied to a pavement LCA to propagate uncertainties throughout an energy and global warming potential (GWP) estimation model; a case of a pavement section in Chicago metropolitan area was used.

Results and discussion

Results indicate that each uncertainty source contributes to the overall energy and GWP output of the LCA. Input uncertainty was shown to have significant impact on overall GWP output; for the example case study, GWP interval was around 50%. Parameter uncertainty results showed that an assumption of ±?10% uniform variation in the model parameter priors resulted in 28% variation in the GWP output. Model-form uncertainty had the lowest impact (less than 10% variation in the GWP). This is because the original energy model is relatively accurate in estimating the energy. However, sensitivity of the model-form uncertainty showed that even up to 180% variation in the results can be achieved due to lower original model accuracies.

Conclusions

Investigating each uncertainty source of the model indicated the importance of the accurate characterization, propagation, and quantification of uncertainty. The outcome of this study proposed independent and relatively easy to implement methods that provide robust grounds for objective model uncertainty analysis for LCA applications. Assumptions on inputs, parameter distributions, and model form need to be justified. Input uncertainty plays a key role in overall pavement LCA output. The proposed model correction method as well as interval analysis were relatively easy to implement. Research is still needed to develop a more generic and simplified MCMC simulation procedure that is fast to implement.

     

Assessing the individual and combined effects of uncertainty and variability sources in comparative LCA of pavements

Purpose

Several efforts have attempted to incorporate the sources of uncertainty and variability into the life cycle assessment (LCA) of pavements. However, no method has been proposed that can simultaneously consider data quality, methodological choices, and variability in inputs and outputs without the need for complementary software. This study aims to develop and implement a flexible method that can be used in the LCA software to assess the effects of these sources on the conclusions.

Methods

A Monte Carlo analysis was conducted and applied in a comparative LCA of pavements to assess the preferred scenario. The uncertainty of the results was first estimated by considering the data quality using the ecoinvent database. Moreover, the variabilities of the materials, construction methods, and repair stages of the pavement life cycle were included in the analysis by assigning continuous uniform probability distributions to each variable. Ultimately, the probability of methodological choices was modeled using uniform distributions and assigning a portion of the area of the distribution to each scenario. The individual and combined effects of these uncertainty and variability sources were assessed in a comparative LCA of asphalt and concrete pavements in a cold region such as Quebec (Canada).

Results and discussion

The results of the Monte Carlo analysis show that the allocation choices can change the environmentally preferred scenario in four midpoint categories. These categories are significantly dominated by the crude oil supply chain. The variability in construction materials and methods can change the preferred scenario in the damage categories, namely, human health and global warming. Additionally, parameter uncertainty has a significant effect on the conclusion of the preferred scenario in ecosystem quality. The worst qualitative scores are given to the geographical uncertainty of the elementary flow that primarily contributes to this category (i.e., zinc). The simultaneous effect of the uncertainty and variability sources prevents the decision-maker from reaching a less uncertain conclusion about ecosystem quality, human health, and global warming effects.

Conclusions

This study demonstrates that it is feasible to assess the cumulative effects of common uncertainty and variability sources using commercial LCA software, including Monte Carlo simulation. Based on the variability and uncertainty of the results, the identification of a certain conclusion is case specific at both the midpoint and endpoint levels. Increasing the quality of the inventory is one solution to decreasing the uncertainties related to human health, ecosystem quality, and global warming regarding pavement LCA. This improvement can be achieved by avoiding the adaptation of a similar process to match the considered process and using practical construction efficiencies and realistic construction materials. The effectiveness of these tasks must be assessed in future studies. It should be noted that these conclusions were determined regardless of the uncertainty in the characterization factors of the impact assessment method.

     

Using Standard Statistics to Consider Uncertainty in Industry-Based Life Cycle Inventory Databases (7 pp)

Goal, Scope and Background Decision-makers demand information about the range of possible outcomes of their actions. Therefore, for developing Life Cycle Assessment (LCA) as a decision-making tool, Life Cycle Inventory (LCI) databases should provide uncertainty information. Approaches for incorporating uncertainty should be selected properly contingent upon the characteristics of the LCI database. For example, in industry-based LCI databases where large amounts of up-to-date process data are collected, statistical methods might be useful for quantifying the uncertainties. However, in practice, there is still a lack of knowledge as to what statistical methods are most effective for obtaining the required parameters. Another concern from the industry's perspective is the confidentiality of the process data. The aim of this paper is to propose a procedure for incorporating uncertainty information with statistical methods in industry-based LCI databases, which at the same time preserves the confidentiality of individual data. Methods The proposed procedure for taking uncertainty in industry-based databases into account has two components: continuous probability distributions fitted to scattering unit process data, and rank order correlation coefficients between inventory flows. The type of probability distribution is selected using statistical methods such as goodness-of-fit statistics or experience based approaches. Parameters of probability distributions are estimated using maximum likelihood estimation. Rank order correlation coefficients are calculated for inventory items in order to preserve data interdependencies. Such probability distributions and rank order correlation coefficients may be used in Monte Carlo simulations in order to quantify uncertainties in LCA results as probability distribution. Results and Discussion A case study is performed on the technology selection of polyethylene terephthalate (PET) chemical recycling systems. Three processes are evaluated based on CO2 reduction compared to the conventional incineration technology. To illustrate the application of the proposed procedure, assumptions were made about the uncertainty of LCI flows. The application of the probability distributions and the rank order correlation coefficient is shown, and a sensitivity analysis is performed. A potential use of the results of the hypothetical case study is discussed. Conclusion and Outlook The case study illustrates how the uncertainty information in LCI databases may be used in LCA. Since the actual scattering unit process data were not available for the case study, the uncertainty distribution of the LCA result is hypothetical. However, the merit of adopting the proposed procedure has been illustrated: more informed decision-making becomes possible, basing the decisions on the significance of the LCA results. With this illustration, the authors hope to encourage both database developers and data suppliers to incorporate uncertainty information in LCI databases.     

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.     

An information‐theoretic approach to estimating the composite genetic effects contributing to variation among generation means: Moving beyond the joint‐scaling test for line cross analysis

The pace and direction of evolution in response to selection, drift, and mutation are governed by the genetic architecture that underlies trait variation. Consequently, much of evolutionary theory is predicated on assumptions about whether genes can be considered to act in isolation, or in the context of their genetic background. Evolutionary biologists have disagreed, sometimes heatedly, over which assumptions best describe evolution in nature. Methods for estimating genetic architectures that favor simpler (i.e., additive) models contribute to this debate. Here we address one important source of bias, model selection in line cross analysis (LCA). LCA estimates genetic parameters conditional on the best model chosen from a vast model space using relatively few line means. Current LCA approaches often favor simple models and ignore uncertainty in model choice. To address these issues we introduce Software for Analysis of Genetic Architecture (SAGA), which comprehensively assesses the potential model space, quantifies model selection uncertainty, and uses model weighted averaging to accurately estimate composite genetic effects. Using simulated data and previously published LCA studies, we demonstrate the utility of SAGA to more accurately define the components of complex genetic architectures, and show that traditional approaches have underestimated the importance of epistasis.     

Good modeling practice for PAT applications: Propagation of input uncertainty and sensitivity analysis

The uncertainty and sensitivity analysis are evaluated for their usefulness as part of the model‐building within Process Analytical Technology applications. A mechanistic model describing a batch cultivation of Streptomyces coelicolor for antibiotic production was used as case study. The input uncertainty resulting from assumptions of the model was propagated using the Monte Carlo procedure to estimate the output uncertainty. The results showed that significant uncertainty exists in the model outputs. Moreover the uncertainty in the biomass, glucose, ammonium and base‐consumption were found low compared to the large uncertainty observed in the antibiotic and off‐gas CO₂ predictions. The output uncertainty was observed to be lower during the exponential growth phase, while higher in the stationary and death phases ‐ meaning the model describes some periods better than others. To understand which input parameters are responsible for the output uncertainty, three sensitivity methods (Standardized Regression Coefficients, Morris and differential analysis) were evaluated and compared. The results from these methods were mostly in agreement with each other and revealed that only few parameters (about 10) out of a total 56 were mainly responsible for the output uncertainty. Among these significant parameters, one finds parameters related to fermentation characteristics such as biomass metabolism, chemical equilibria and mass‐transfer. Overall the uncertainty and sensitivity analysis are found promising for helping to build reliable mechanistic models and to interpret the model outputs properly. These tools make part of good modeling practice, which can contribute to successful PAT applications for increased process understanding, operation and control purposes. © 2009 American Institute of Chemical Engineers Biotechnol. Prog., 2009     

Evaluation of habitat suitability index models by global sensitivity and uncertainty analyses: a case study for submerged aquatic vegetation

Habitat suitability index (HSI) models are commonly used to predict habitat quality and species distributions and are used to develop biological surveys, assess reserve and management priorities, and anticipate possible change under different management or climate change scenarios. Important management decisions may be based on model results, often without a clear understanding of the level of uncertainty associated with model outputs. We present an integrated methodology to assess the propagation of uncertainty from both inputs and structure of the HSI models on model outputs (uncertainty analysis: UA) and relative importance of uncertain model inputs and their interactions on the model output uncertainty (global sensitivity analysis: GSA). We illustrate the GSA/UA framework using simulated hydrology input data from a hydrodynamic model representing sea level changes and HSI models for two species of submerged aquatic vegetation (SAV) in southwest Everglades National Park: Vallisneria americana (tape grass) and Halodule wrightii (shoal grass). We found considerable spatial variation in uncertainty for both species, but distributions of HSI scores still allowed discrimination of sites with good versus poor conditions. Ranking of input parameter sensitivities also varied spatially for both species, with high habitat quality sites showing higher sensitivity to different parameters than low‐quality sites. HSI models may be especially useful when species distribution data are unavailable, providing means of exploiting widely available environmental datasets to model past, current, and future habitat conditions. The GSA/UA approach provides a general method for better understanding HSI model dynamics, the spatial and temporal variation in uncertainties, and the parameters that contribute most to model uncertainty. Including an uncertainty and sensitivity analysis in modeling efforts as part of the decision‐making framework will result in better‐informed, more robust decisions.     

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)     

Stimulus design for model selection and validation in cell signaling

Mechanism-based chemical kinetic models are increasingly being used to describe biological signaling. Such models serve to encapsulate current understanding of pathways and to enable insight into complex biological processes. One challenge in model development is that, with limited experimental data, multiple models can be consistent with known mechanisms and existing data. Here, we address the problem of model ambiguity by providing a method for designing dynamic stimuli that, in stimulus–response experiments, distinguish among parameterized models with different topologies, i.e., reaction mechanisms, in which only some of the species can be measured. We develop the approach by presenting two formulations of a model-based controller that is used to design the dynamic stimulus. In both formulations, an input signal is designed for each candidate model and parameterization so as to drive the model outputs through a target trajectory. The quality of a model is then assessed by the ability of the corresponding controller, informed by that model, to drive the experimental system. We evaluated our method on models of antibody–ligand binding, mitogen-activated protein kinase (MAPK) phosphorylation and de-phosphorylation, and larger models of the epidermal growth factor receptor (EGFR) pathway. For each of these systems, the controller informed by the correct model is the most successful at designing a stimulus to produce the desired behavior. Using these stimuli we were able to distinguish between models with subtle mechanistic differences or where input and outputs were multiple reactions removed from the model differences. An advantage of this method of model discrimination is that it does not require novel reagents, or altered measurement techniques; the only change to the experiment is the time course of stimulation. Taken together, these results provide a strong basis for using designed input stimuli as a tool for the development of cell signaling models.     

Attributional and Consequential Environmental Assessment of the Shift to Lead-Free Solders (10 pp)

- Goal, Scope, Background. As of July 1st, 2006, lead will be banned in most solder pastes used in the electronics industry. This has called for environmental evaluation of alternatives to tin-lead solders. Our life cycle assessment (LCA) has two aims: (i) to compare attributional and consequential LCA methodologies, and (ii) to compare a SnPb solder (62% tin, 36% lead, 2% silver) to a Pb-free solder (95.5% tin, 3.8% silver, 0.7% copper). Methods An attributional LCA model describes the environmental impact of the solder life cycle. Ideally, it should include average data on each unit process within the life cycle. The model does not include unit processes other than those of the life cycle investigated, but significant cut-offs within the life cycle can be avoided through the use of environmentally expanded input-output tables. A consequential LCA model includes unit processes that are significantly affected irrespective of whether they are within or outside the life cycle. Ideally, it should include marginal data on bulk production processes in the background system. Our consequential LCA model includes economic partial equilibrium models of the lead and scrap lead markets. However, both our LCA models are based on data from the literature or from individual production sites. The partial equilibrium models are based on assumptions. The life cycle impact assessment is restricted to global warming potential (GWP). Results and Discussion The attributional LCA demonstrates the obvious fact that the shift from SnPb to Pb-free solder means that lead is more or less eliminated from the solder life cycle. The attributional LCA results also indicate that the Pb-free option contributes 10% more to the GWP than SnPb. Despite the poor quality of the data, the consequential LCA demonstrates that, when lead use is eliminated from the solder life cycle, the effect is partly offset by increased lead use in batteries and other products. This shift can contribute to environmental improvement because lead emissions are likely to be greatly reduced, while batteries can contribute to reducing GWP, thereby offsetting part of the GWP increase in the solder life cycle. Conclusions The shift from SnPb to Pb-free solder is likely to result in reduced lead emissions and increased GWP. Attributional and consequential LCAs yield complementary knowledge on the consequences of this shift in solder pastes. At present, consequential LCA is hampered by the lack of readily available marginal data and the lack of input data to economic partial equilibrium models. However, when the input to a consequential LCA model is in the form of quantitative assumptions based on a semi-qualitative discussion, the model can still generate new knowledge. Recommendations and Outlook Experts on partial equilibrium models should be involved in consequential LCA modeling in order to improve the input data on price elasticity, marginal production, and marginal consumption.     

Improving the integrated hybrid LCA in the upstream scope 3 emissions inventory analysis

Purpose

The protocols of carbon footprints generally define three scopes for different greenhouse gas (GHG) emissions levels. The most important carbon footprint emissions source comes from upstream indirect emissions of scope 3 for products that do not consume energy during their use phase. Upstream scope 3 GHG inventory can usually be analyzed through input–output or hybrid LCA analysis. The economic input–output life cycle analysis (EIO-LCA) and the hybrid LCA model have been widely used for this purpose. However, a cutoff error exists in the hybrid model, and the lack of a truncation criterion between process and IO inventory may lead to a high level of uncertainty in the hybrid model. This study attempts to improve the problem of cutoff uncertainty in hybrid LCA and proposes a method to minimize the cutoff uncertainty.

Methods

The way to improve the cutoff uncertainty could follow two steps. First, through the IO inventory analysis of EIO-LCA, we can define the emissions by various tiers of product components. The IO inventory indicator can provide a definitive criterion for the process inventory of the hybrid model. Second, we connect the process- and IO-LCI according to the IO inventory result. The advantage of the process inventory is that it provides detailed manufacturing information on the target while the IO encompasses a complete system boundary. For improvements, the process inventory can catch the most important process of the GHG emissions, and the IO inventory could compensate for the remainder of the incomplete system inventory.

Results and discussion

In this case study, the printed circuit board production process is used to evaluate the efficiency of the improved method. The threshold M was set to 70 in this case study, and the IO inventory provides the remaining 30 %. For the integrated hybrid model, the tier 3 process inventory takes only 64 % while the incorporation of the proposed method can include 92 % of the total emissions, which shows the cutoff uncertainty can be reduced through the improvement.

Conclusions

This study provides a clear guideline for process and IO cutoff criteria, which can help the truncation uncertainty. When higher precision is required, process LCI will need to play an important role, and thus, a higher M value should be set. In this situation, the emissions from IO-LCI would be smaller than the emissions from the process LCI. The appropriate solution would attain a comfortable balance between data accuracy and time and labor consumption.     

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].     

Designing hybrid life cycle assessment models based on uncertainty and complexity

Purpose

Despite the wide use of LCA for environmental profiling, the approach for determining the system boundary within LCA models continues to be subjective and lacking in mathematical rigor. As a result, life cycle models are often developed in an ad hoc manner, and are difficult to compare. Significant environmental impacts may be inadvertently left out. Overcoming this shortcoming can help elicit greater confidence in life cycle models and their use for decision making.

Methods

This paper describes a framework for hybrid life cycle model generation by selecting activities based on their importance, parametric uncertainty, and contribution to network complexity. The importance of activities is determined by structural path analysis—which then guides the construction of life cycle models based on uncertainty and complexity indicators. Information about uncertainty is from the available life cycle inventory; complexity is quantified by cost or granularity. The life cycle model is developed in a hierarchical manner by adding the most important activities until error requirements are satisfied or network complexity exceeds user-specified constraints.

Results and Discussion

The framework is applied to an illustrative example for building a hybrid LCA model. Since this is a constructed example, the results can be compared with the actual impact, to validate the approach. This application demonstrates how the algorithm sequentially develops a life cycle model of acceptable uncertainty and network complexity. Challenges in applying this framework to practical problems are discussed.

Conclusion

The presented algorithm designs system boundaries between scales of hybrid LCA models, includes or omits activities from the system based on path analysis of environmental impact contribution at upstream network nodes, and provides model quality indicators that permit comparison between different LCA models.

     

Accounting for inventory data and methodological choice uncertainty in a comparative life cycle assessment: the case of integrated multi-trophic aquaculture in an offshore Mediterranean enterprise

Purpose

Integrated multi-trophic aquaculture (IMTA), growing different species in the same space, is a technology that may help manage the environmental impacts of coastal aquaculture. Nutrient discharges to seawater from monoculture aquaculture are conceptually minimized in IMTA, while expanding the farm economic base. In this study, we investigate the environmental trade-offs for a small-to-medium enterprise (SME) considering a shift from monoculture towards IMTA production of marine fish.

Methods

A comparative life cycle assessment (LCA), including uncertainty analysis, was implemented for an aquaculture SME in Italy. Quantification and simultaneous propagation of uncertainty of inventory data and uncertainty due to the choice of allocation method were combined with dependent sampling to account for relative uncertainties and statistical testing and interpretation to understand the uncertainty analysis results. Monte Carlo simulations were used as a propagation method. The environmental impacts per kilo of fish produced in monoculture and in IMTA were compared. Twelve impact categories were considered. The comparison is first made excluding uncertainty (deterministic LCA) and then accounting for uncertainties.

Results and discussion

Deterministic LCA results evidence marginal differences between the impacts of IMTA and monoculture fish production. IMTA performs better on all impacts studied. However, statistical testing and interpretation of the uncertainty analysis results showed that only mean impacts for climate change are significantly different for both productive systems, favoring IMTA. For the case study, technical variables such as scales of production of the species from different trophic levels, their integration (space and time), and the choice of species determine the trade-offs. Also, LCA methodological choices such as that for an allocation method and the treatment of relative uncertainties were determinant in the comparison of environmental trade-offs.

Conclusions

The case study showed that environmental trade-offs between monoculture and IMTA fish production depend on technical variables and methodological choices. The combination of statistical methods to quantify, propagate, and interpret uncertainty was successfully tested. This approach supports more robust environmental trade-off assessments between alternatives in LCAs with uncertainty analysis by adding information on the significance of results. It was difficult to establish whether IMTA does bring benefits given the scales of production in the case study. We recommend that the methodology defined here is applied to fully industrialized IMTA systems or bay-scale environments, to provide more robust conclusions about the environmental benefits of this aquaculture type in Europe.

     

Environmental assessment of products

The evaluation of product alternatives in Life Cycle Analysis (LCA) is a critical step on the basis of results as related to their impact category data. Decisions involving several environmental issues are hardly ever straightforward, since one alternative only seldom clearly dominates the others in all aspects. More often, one alternative scores better on some environmental issues and worse on others. A combination of impact data and preferences is then required for evaluation. This can be done using evaluation methods based on fixed societal preferences. However, by applying different evaluation methods to the same data, different “best” alternatives may be chosen. This reduces the credibility of LCA results. Instead of fixed societal preferences an approach has been developed which uses consensus-oriented ranges of societal values for specifying the ranking of the overall environmental attractiveness of alternatives. These ranges may indicate both the uncertainty of decision-makers and the shifting of societal values, e.g. as related to the dynamics of knowledge of environmental problem areas. In this article, an approach is proposed which combines environmental data and uncertain societal values to form a clear statement on alternatives regarding their overall damage. By using a full set of potentially relevant societal preferences, a merely coincidental selection of the best product alternative is ruled out. A step-by-step procedure, narrowing down the feasible range of societal preferences, has been developed. The approach is illustrated using a case study of TV-housing concepts and a survey.     

Life-Cycle Assessment: Constraints on Moving from Inventory to Impact Assessment

Life-cycle assessment (LCA) is a technique for systematically analyzing a product from cradle-to-grave, that is, from resource extraction through manufacture and use to disposal. LCA is a mixed or hybrid analytical system. An inventory phase analyzes system inputs of energy and materials along with outputs of emissions and wastes throughout life cycle, usually as quantitative mass loadings. An impact assessment phase then examines these loadings in light of potential environmental issues using a mixed spectrum of qualitative and quantitative methods. The constraints imposed by inventory's loss of spatial, temporal, dose-response, and threshold information raise concerns about the accuracy of impact assessment. The degree of constraint varies widely according to the environmental issue in question and models used to extrapolate the inventory data. LCA results may have limited value in two areas: (I) local and/ortransient biophysical processes and (2) issues involving biological parameters, such as biodiversity, habitat alteration, and toxicity. The end result is that impact assessment does not measure actual effects or impacts, nor does it calculate the likelihood of an effect or risk Rather, LCA impact assessment results are largely directional environmental indicaton. The accuracy and usefulness of indicators need to be assessed individually and in a circumstance-specific manner prior to decision making. This limits LCAs usefulness as the sole basis for comprehensive assessments and the comparisons of alternatives. In conclusion, LCA may identify potential issues from a systemwide perspective, but more-focused assessments using other analytical techniques are often necessary to resolve the issues.     

Generalized Make and Use Framework for Allocation in Life Cycle Assessment

Allocation in life cycle inventory (LCI) analysis is one of the long‐standing methodological issues in life cycle assessment (LCA). Discussion on allocation among LCA researchers has taken place almost in complete isolation from the series of closely related discussions from the 1960s in the field of input?output economics, regarding the supply and use framework. This article aims at developing a coherent mathematical framework for allocation in LCA by connecting the parallel developments of the LCA and the input?output communities. In doing so, the article shows that the partitioning method in LCA is equivalent to the industry‐technology model in input?output economics, and system expansion in LCA is equivalent to the by‐product‐technology model in input?output output economics. Furthermore, we argue that the commodity‐technology model and the by‐product‐technology model, which have been considered as two different models in input?output economics for more than 40 years, are essentially equivalent when it comes to practical applications. It is shown that the matrix‐based approach used for system expansion successfully solves the endless regression problem that has been raised in LCA literature. A numerical example is introduced to demonstrate the use of allocation models. The relationship of these approaches with consequential and attributional LCA models is also discussed.