Three Strategies to Overcome the Limitations of Life-Cycle Assessment

Many research efforts aim at an extension of life‐cycle assessment (LCA) in order to increase its spatial or temporal detail or to enlarge its scope. This is an important contribution to industrial ecology as a scientific discipline, but from the application viewpoint other options are available to obtain more detailed information, or to obtain information over a broader range of impacts in a life‐cycle perspective. This article discusses three different strategies to reach these aims: (1) extension of LCA—one consistent model; (2) use of a toolbox—separate models used in combination; and (3) hybrid analysis—combination of models with data flows between them. Extension of LCA offers the most consistent solution. Developments in LCA are moving toward greater spatial detail and temporal resolution and the inclusion of social issues. Creating a supertool with too many data and resource requirements is, however, a risk. Moreover, a number of social issues are not easily modeled in relation to a functional unit. The development of a toolbox offers the most flexibility regarding spatial and temporal information and regarding the inclusion of other types of impacts. The rigid structure of LCA no longer sets limits; every aspect can be dealt with according to the logic of the relevant tool. The results lack consistency, however, preventing further formal integration. The third strategy, hybrid analysis, takes up an intermediate position between the other two. This strategy is more flexible than extension of LCA and more consistent than a toolbox. Hybrid analysis thus has the potential to combine the strong points of the other two strategies. It offers an interesting path for further discovery, broader than the already well‐known combination of process‐LCA and input‐output‐LCA. We present a number of examples of hybrid analysis to illustrate the potentials of this strategy. Developments in the field of a toolbox or of hybrid analysis may become fully consistent with LCA, and then in fact become part of the first solution, extension of LCA.     

Regionalization of agri-food life cycle assessment: a review of studies in Portugal and recommendations for the future

Purpose

One of the main trends in life cycle assessment (LCA) today is towards increased regionalization in inventories and impact assessment methods. LCA studies require the collection of activity data but also of increasingly region-specific background data to accurately depict supply chain processes and enable the application of an increasing number of geographically explicit impact assessment models. This is particularly important for agri-food products. In this review, we assess progress in Portugal towards this goal and provide recommendations for future developments.

Methods

We perform a comprehensive review of available LCA studies conducted for Portuguese agri-food products, in order to evaluate the current state of Portuguese agri-food LCA. Among other issues, we assess availability of data, methods used, level of regionalization, impact assessment model relevance and coherence for inter-product comparability. We also provide conclusions and recommendations based on recent developments in the field.

Results and discussion

We found 22 LCA studies, covering 22 different products. The analysis of these studies reveals limitations in inter-study comparability. The main challenges have to do with a lack of country-specific foreground data sources applied consistently in the studies found, with discrepancies in impact assessment categories, and with the use of simple functional units that may misrepresent the product analyzed.

Conclusions

We conclude that Portuguese agri-food LCA studies do not have a systematic and country-scale approach in order to guarantee regional accuracy and comparability. We propose a research strategy to engage the Portuguese agri-food LCA community in devising a consistent framework before practical application studies are conducted.

     

Environmental footprinting of hospitals: Organizational life cycle assessment of a Canadian hospital

Healthcare is a critical and complex service sector with direct and indirect greenhouse gas (GHG) emissions amounting to 5%–10% of the national total in developed economies like Canada and the United States. Along with a growing, albeit sporadic, set of life cycle assessment (LCA) (and “carbon footprinting”) studies of specific medical products and procedures, there is growing interest in “environmental footprinting” of hospitals. In this article, we advance this rapidly evolving area through a comprehensive organizational LCA of a 40-bed hospital in British Columbia, Canada, in its 2019 fiscal year. Our results indicate that the total environmental footprint of the hospital includes, among other things, global warming potential of 3500–5000 t CO₂ eq. (with 95% confidence). “Hotspots” in this footprint are attributable to energy and water use (and wastewater released), releases of anesthetic gases (which are potent GHGs), and the upstream production of the thousands of materials, chemicals, pharmaceuticals, and other products used in the hospital. The generalizability and comparability of these results are limited by inconsistencies across the few environmental footprinting studies of hospitals conducted to date. Nonetheless, our novel methodological approach, in which we compiled new LCA data for 200 goods and services used in healthcare—strategically selected to statistically represent the 2927 unique products in the hospital's “supply-chains”—has broad applicability in healthcare and beyond.     

Enabling optimization in LCA: from “ad hoc” to “structural” LCA approach—based on a biodiesel well-to-wheel case study

Purpose

Applied life cycle assessment (LCA) studies often lead to a comparison of rather few alternatives; we call this the “ad hoc LCA approach.” This can seem surprising since applied LCAs normally cover countless options for variations and derived potentials for improvements in a product life cycle. In this paper, we will suggest an alternative approach to the ad hoc approach, which more systematically addresses the many possible variations to identify the most promising. We call it the “structural LCA approach.” The goals of this paper are (1) to provide basic guidelines for the structural approach, including an easy expansion of the LCA space; (2) to show that the structural LCA approach can be used for different types of optimization in LCA; and (3) to improve the transparency of the LCA work.

Methods

The structural approach is based on the methodology “design of experiments” (Montgomery 2005). Through a biodiesel well-to-wheel study, we demonstrate a generic approach of applying explanatory variables and corresponding impact categories within the LCA methodology. Explanatory variables are product system variables that can influence the environmental impacts from the system. Furthermore, using the structural approach enables two different possibilities for optimization: (1) single-objective optimization (SO) based on response surface methodology (Montgomery 2005) and (2) multiobjective optimization (MO) by the hypervolume estimation taboo search (HETS) method. HETS enables MO for more than two or three objectives.

Results and discussion

Using SO, the explanatory variable “use of residual straw from fields” is, by far, the explanatory variable that can contribute with the highest decrease of climate change potential. For the respiratory inorganics impact category, the most influencing explanatory variable is found to be the use of different alcohol types (bioethanol or petrochemical methanol) in biodiesel production. Using MO, we found the Pareto front based on 5 different life cycle pathways which are nondominated solutions out of 66 different analyzed solutions. Given that there is a fixed amount of resources available for the LCA practitioner, it becomes a prioritizing problem whether to apply the structural LCA approach or not. If the decision maker only has power to change a single explanatory variable, it might not be beneficial to apply the structural LCA approach. However, if the decision maker (such as decision makers at the societal level) has power to change more explanatory variables, then the structural LCA approach seems beneficial for quantifying and comparing the potentials for environmental improvement between the different explanatory variables in an LCA system and identifying the overall most promising product system configurations among the chosen PWs.

Conclusions

The implementation of the structural LCA approach and the derived use of SO and MO have been successfully achieved and demonstrated in the present paper. In addition, it is demonstrated that the structural LCA approach can lead to more transparent LCAs since the potentially most important explanatory variables which are used to model the LCAs are explicitly presented through the structural LCA approach. The suggested structural approach is a new approach to LCA and it seems to be a promising approach for searching or screening product systems for environmental optimization potentials. In the presented case, the design has been a rather simple full factorial design. More complicated problems or designs, such as fractional designs, nested designs, split plot designs, and/or unbalanced data, in the context of LCA could be investigated further using the structural approach.     

Confronting Uncertainty in Life Cycle Assessment Used for Decision Support

The aim of this article is to help confront uncertainty in life cycle assessments (LCAs) used for decision support. LCAs offer a quantitative approach to assess environmental effects of products, technologies, and services and are conducted by an LCA practitioner or analyst (AN) to support the decision maker (DM) in making the best possible choice for the environment. At present, some DMs do not trust the LCA to be a reliable decision‐support tool—often because DMs consider the uncertainty of an LCA to be too large. The standard evaluation of uncertainty in LCAs is an ex‐post approach that can be described as a variance simulation based on individual data points used in an LCA. This article develops and proposes a taxonomy for LCAs based on extensive research in the LCA, management, and economic literature. This taxonomy can be used ex ante to support planning and communication between an AN and DM regarding which type of LCA study to employ for the decision context at hand. This taxonomy enables the derivation of an LCA classification matrix to clearly identify and communicate the type of a given LCA. By relating the LCA classification matrix to statistical principles, we can also rank the different types of LCA on an expected inherent uncertainty scale that can be used to confront and address potential uncertainty. However, this article does not attempt to offer a quantitative approach for assessing uncertainty in LCAs used for decision support.     

Developing a systematic framework for consistent allocation in LCA

Purpose

Multifunctionality in life-cycle assessment (LCA) is solved with allocation, for which many different procedures are available. Lack of sufficient guidance and difficulties to identify the correct allocation approach cause a large number of combinations of methods to exist in scientific literature. This paper reviews allocation procedures for recycling situations, with the aim to identify a systematic approach to apply allocation.

Methods

Assumptions and definitions for the most important terms related to multifunctionality and recycling in LCA are given. The most relevant allocation procedures are identified from literature. These procedures are expressed in mathematical formulas and schemes and arranged in a systematic framework based on the underlying objectives and assumptions of the procedures.

Results and discussion

If the LCA goal asks for an attributional approach, multifunctionality can be solved by applying system expansion—i.e. including the co-functions in the functional unit—or partitioning. The cut-off approach is a form of partitioning, attributing all the impacts to the functional unit. If the LCA goal asks for a consequential approach, substitution is applied, for which three methods are identified: the end-of-life recycling method and the waste mining method, which are combined in the 50/50 method. We propose to merge these methods in a new formula: the market price-based substitution method. The inclusion of economic values and maintaining a strict separation between attributional and consequential LCA are considered to increase realism and consistency of the LCA method.

Conclusions and perspectives

We identified the most pertinent allocation procedures—for recycling as well as co-production and energy recovery—and expressed them in mathematical formulas and schemes. Based on the underlying objectives of the allocation procedures, we positioned them in a systematic and consistent framework, relating the procedures to the LCA goal definition and an attributional or consequential approach. We identified a new substitution method that replaces the three existing methods in consequential LCA. Further research should test the validity of the systematic framework and the market price-based substitution method by means of case studies.

     

Comparison of Tools for Quantifying the Environmental Performance of an Urban Territory

To support effective urban policies aimed at decreasing the environmental impacts of cities, it is important to develop robust tools for accounting those impacts. Environmentally extended input‐output analysis (EEIOA) is among the most used tools for this purpose, allowing the quantification of both direct and indirect impacts. Life cycle assessment (LCA) is also a holistic and comprehensive tool that accounts for direct and indirect impacts—but its application to cities is still very recent. This study aims at applying EEIOA and LCA to the municipality of Aveiro (Portugal) in order to compare the outcomes of the two tools in terms of total impacts (climate change and fossil fuel depletion) and hotspots (sectors/products contributing most to the impacts), to identify limitations and advantages of the tools when applied to Aveiro, and to illustrate how LCA can be applied to cities. The total impacts estimated with LCA and EEIOA were similar and the hotspots were also the same: transports, food, construction, and electricity. However, the relative contribution of some sectors was very different in the two tools due to methodological differences mainly in system boundaries, type of activities or products considered in each sector, and geographical coverage of impact data. This study concludes that the analyzed tools can provide complementary results to support decision making concerning urban planning and management.     

Comment on the editorial note by Baitz et XXI aliis

Background

The editor of this journal has been waiting for such a contribution of the life cycle assessment (LCA) practitioners and users for years, since the last debate of this kind dates back to beginning of the new century. It is remembered as the “Two planets debate” and coincided with the emergence of life cycle management, i.e. the use of life-cycle based methods in industry.

The “Two planets”

This is a metaphor coined at the Society of Environmental Toxicology and Chemistry (SETAC) Europe case studies symposium 2000 and designates the fact that many academic LCA developers and the LCA practitioners seem to live in different spheres. The editorial note by Baitz et al. shows that this seems to be true still today. It is argued that the practitioners do not frequently enough participate in the working groups organized by SETAC, the UNEP/SETAC life cycle initiative and other international organizations and therefore cannot bring in the practical experience they have acquired in performing “real-life” LCA studies. The new LCIA methods, for instance, are often not accepted by the LCA practitioners and commissioners, since essential aspects were not recognised during method development.

Tentative proposal for a solution

The solution of the problems pointed out in Baitz et al. cannot be to hinder the inhabitants of the academic planet in inventing ingenious new methods for reasons of academic freedom. It is proposed that new methods developed should be tested by practitioners in real-life LCA studies. Data asymmetries in comparative (i.e. most) LCA studies using more demanding methods may shift problems from LCIA to the LCI databases. With regard to the financing of such studies, it should be remembered that practitioners do their living by performing LCAs and other studies and have to calculate a full overhead in addition to the pure working costs.     

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.     

Sensitivity coefficient-based uncertainty analysis for multi-functionality in LCA

Purpose

In LCA, a multi-functionality problem exists whenever the environmental impacts of a multi-functional process have to be allocated between its multiple functions. Methods for fixing this multi-functionality problem are controversially discussed because the methods include ambiguous choices. To study the influence of these choices, the ISO standard requires a sensitivity analysis. This work presents an analytical method for analyzing sensitivities and uncertainties of LCA results with respect to the choices made when a multi-functionality problem is fixed.

Methods

The existing matrix algebra for LCA is expanded by explicit equations for methods that fix multi-functionality problems: allocation and avoided burden. For allocation, choices exist between alternative allocation factors. The expanded equations allow calculating LCA results as a function of allocation factors. For avoided burden, choices exist in selecting an avoided burden process from multiple candidates. This choice is represented by so-called aggregation factors. For avoided burden, the expanded equations calculate LCA results as a function of aggregation factors. The expanded equations are used to derive sensitivity coefficients for LCA results with respect to allocation factors and aggregation factors. Based on the sensitivity coefficients, uncertainties due to fixing a multi-functionality problem by allocation or avoided burden are analytically propagated. The method is illustrated using a virtual numerical example.

Results and discussion

The presented approach rigorously quantifies sensitivities of LCA results with respect to the choices made when multi-functionality problems are fixed with allocation and avoided burden. The uncertainties due to fixing multi-functionality problems are analytically propagated to uncertainties in LCA results using a first-order approximation. For uncertainties in allocation factors, the first-order approximation is exact if no loops of the allocated functional flows exist. The contribution of uncertainties due to fixing multi-functionality problems can be directly compared to the uncertainty contributions induced by uncertain process data or characterization factors. The presented method allows the computationally efficient study of uncertainties due to fixing multi-functionality problems and could be automated in software tools.

Conclusions

This work provides a systematic method for the sensitivity analysis required by the ISO standard in case choices between alternative allocation procedures exist. The resulting analytical approach includes contributions of uncertainties in process data, characterization factors, and—in extension to existing methods—uncertainties due to fixing multi-functionality problems in a unifying rigorous framework. Based on the uncertainty contributions, LCA practitioners can select fields for data refinement to decrease the overall uncertainty in LCA results.     

Error propagation methods for LCA—a comparison

Purpose

The analysis of uncertainty in life cycle assessment (LCA) studies has been a topic for more than 10 years, and many commercial LCA programs now feature a sampling approach called Monte Carlo analysis. Yet, a full Monte Carlo analysis of a large LCA system, for instance containing the 4,000 unit processes of ecoinvent v2.2, is rarely carried out by LCA practitioners. One reason for this is computation time. An alternative faster than Monte Carlo method is analytical error propagation by means of a Taylor series expansion; however, this approach suffers from being explained in the literature in conflicting ways, hampering implementation in most software packages for LCA. The purpose of this paper is to compare the two different approaches from a theoretical and practical perspective.

Methods

In this paper, we compare the analytical and sampling approaches in terms of their theoretical background and their mathematical formulation. Using three case studies—one stylized, one real-sized, and one input–output (IO)-based—we approach these techniques from a practical perspective and compare them in terms of speed and results.

Results

Depending on the precise question, a sampling or an analytical approach provides more useful information. Whenever they provide the same indicators, an analytical approach is much faster but less reliable when the uncertainties are large.

Conclusions

For a good analysis, analytical and sampling approaches are equally important, and we recommend practitioners to use both whenever available, and we recommend software suppliers to implement both.     

The purpose of LCA in environmental labels and concepts of products

This very interesting Discussion Forum showed the different points of view of basic science and applied science in practice concerning environmental labeling and the lacking comparability between them. Practitioners and consumers stated the large amount of labels. At this point environmental key-parameter models like the one presented for green electricity could be an option. For LCA-researchers it was clear that environmental labeling ought to be connected to an LCA or a comparable environmental valuation method. ISO offers four types of labels and if every label on the market would be ISO-conform and declared as such, comparisons would be much easier. Practitioners from companies stated that a lot of environmental data about there products gained from LCA or similar methods is available, but that the consumer is not yet interested in this kind of information. But in any case their companies will go on issuing environmental declarations for products, hoping that in the long run consumer’s interest and choice will include environmental performance. The discussion in the panel also showed that social aspects are not preferably integrated in an environmental declaration but separate in a social declaration because the evaluation methods are totally different. In any case, the interest of the consumer in the social circumstances of the production of goods is steadily increasing.     

Investigating transparency regarding ecoinvent users’ system model choices

Purpose

Life cycle assessment (LCA) is a data-intensive methodology; therefore, experts usually focus collection efforts on a few activities, while generic data on remaining activities are taken from databases. Even though increased availability of databases has facilitated LCA takeoff, assuring data quality is fundamental to ensure meaningful results and reliable interpretation.

Methods

Ecoinvent has become a global reference for inventory data. Its current version released three impact partition modeling options—the recycled content, “allocation at the point of substitution” (APOS), and consequential models—whose adequate choice is crucial for yielding meaningful assessments. Tutorials and manuals describe the distribution algorithm that backs each system model, to ground decision-making regarding the best fit to a study’s goals. We performed a systematic literature review to investigate—within the papers published on the International Journal of LCA (IJLCA)—how transparently authors addressed the system model choices.

Results and discussion

About 70% of LCA practitioners continued to use earlier versions of ecoinvent after version 3 was launched in 2013. The number of papers using versions 3.x only showed an increased growth trend 2 years later. Eighty-three papers actually adopted the newest version of the database. From those, only 29 papers clearly mentioned the adopted system model. Our SLR also suggests a trend regarding authorship profile of LCA-related studies: the number of studies conducted by practitioners aware of the intricacies of sound modeling of background and foreground data might have been surpassed by those conducted by non-LCA specialists who use LCA as a supporting tool for investigations in applied fields, and merely scratch the surface.

Conclusions

Our results point to a need for a caveat: ecoinvent users must take time to understand the general concept behind each system model and practice one of the most important actions when performing an LCA—state methodological choices clearly.

     

Achieving consistency in life cycle assessment practice within the European construction sector: the role of the EeBGuide InfoHub

Purpose

The objective of the paper is to discuss the role of a new guidance document for life cycle assessment (LCA) in the construction sector available as an online InfoHub.

Methods

This InfoHub derives from the EeBGuide European project that aimed at developing a guidance document for energy-efficient building LCA studies. The InfoHub is built on reference documents such as the ISO 14040-44 standards, the EN 15804 and EN 15978 standards as well as the ILCD Handbook. The guidance document was filled with expertise and knowledge of several experts. The focus was put on providing scientifically sound, yet practical guidance.

Results

The EeBGuide InfoHub is an online guidance document, setting rules for conducting LCA studies and giving instructions on how to do this. The document has a section on buildings—new and existing—and a section on construction products. It is structured according to the life cycle stages of the European standards EN 15804 and EN 15978, covering all aspects of LCA studies by applying provisions from these standards and the ILCD handbook, wherever applicable. The guidance is presented for different scopes of studies by means of three study types. For the same system boundaries, default values are proposed in early or quick assessment (screening and simplified LCA) while detailed calculation rules correspond to a complete LCA. Such approach is intended to better match the user needs in the building sector.

Conclusions and recommendations

This paper can be viewed as a contribution to the ongoing efforts to improve the consistency and harmonisation in LCA studies for building products and buildings. Further contributions are now needed to improve building LCA guidance and to strengthen links between research, standardisation and implementation of LCA in the construction practice.     

Framework for LCI modelling of releases of manufactured nanomaterials along their life cycle

Purpose

Numerous publications in the last years stressed the growing importance of nanotechnology in our society, highlighting both positive as well as in the negative topics. Life cycle assessment (LCA) is amongst the most established and best-developed tool in the area of product-related assessment. In order to use this tool in the area of nanotechnology, clear rules of how emissions of nanomaterials should be taken into account on the level of life cycle inventory (LCI) modelling are required—i.e. what elements and properties need to be reported for an emission of a nanomaterial. The objective of this paper is to describe such a framework for an adequate and comprehensive integration of releases of nanomaterials.

Methods

With a three-step method, additional properties are identified that are necessary for an adequate integration of releases of nanomaterials into LCA studies.

Result and discussion

In the first step, a comprehensive characterisation of the release of a nanomaterial is compiled—based on reviewing scientific publications, results from expert workshops and publications from public authorities and international organisations. In the second step, this comprehensive overview is refined to a list containing only those properties that are effectively relevant for LCA studies—i.e. properties that influence the impacts in the areas of human toxicity and ecotoxicity, respectively. For this, an academic approach is combined with a second, more practical, view point, resulting together in a prioritisation of this list of properties. Finally, in a third step, these findings are translated into the LCA language—by showing how such additional properties could be integrated into the current LCA data formats for a broader use by the LCA community.

Conclusions

As a compromise between scholarly knowledge and the (toxicological) reality, this paper presents a clear proposal of an LCI modelling framework for the integration of releases of nanomaterials in LCA studies. However, only the broad testing of this framework in various situations will show if the suggested simplifications and reductions keep the characterisation of releases of nanomaterials specific enough and/or if assessment is accurate enough. Therefore, a next step has to come from the impact assessment, by the development of characterisation factors as a function of size and shape of such releases.     

Life cycle assessment of emerging technologies: A review

In recent literature, prospective application of life cycle assessment (LCA) at low technology readiness levels (TRL) has gained immense interest for its potential to enable development of emerging technologies with improved environmental performances. However, limited data, uncertain functionality, scale up issues and uncertainties make it very challenging for the standard LCA guidelines to evaluate emerging technologies and requires methodological advances in the current LCA framework. In this paper, we review published literature to identify major methodological challenges and key research efforts to resolve these issues with a focus on recent developments in five major areas: cross‐study comparability, data availability and quality, scale‐up issues, uncertainty and uncertainty communication, and assessment time. We also provide a number of recommendations for future research to support the evaluation of emerging technologies at low technology readiness levels: (a) the development of a consistent framework and reporting methods for LCA of emerging technologies; (b) the integration of other tools with LCA, such as multicriteria decision analysis, risk analysis, technoeconomic analysis; and (c) the development of a data repository for emerging materials, processes, and technologies.     

Life cycle assessment standards

The newly emerging LCA standards provide an opportunity to review and improve upon the current LCA methodology. As more industrial practitioners enter the arena, the opportunity arises to not only demand environmental improvement from industrial service and product providers but also to fill LCA data gaps. A framework is suggested for improvement in the current LCA framework that focuses on the business relationships of the industrial practitioner. The framework seeks to promote environmental improvement from industrial sectors through the identification of state-of-the-art technologies used throughout a life cycle. Basing LCAs on the best performers in an industry will create a market for a high level of environmental performance, disperse the responsibility of inventory data gathering, and improve upon the advancements already anticipated through the widespread application of LCA.     

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.