Post-consumer waste wood in attributive product LCA

Background  

In product life cycle assessment (LCA), the attribution of environmental interventions to a product under study is an ambiguous task. This is due to a) the simplistic modeling characteristics in the life cycle inventory step (LCI) of LCA in view of the complexity of our techno-economic system, and b) to the nontangible theoretical nature of the product system as a representation of the processes ‘causally’ linked to a product. Ambiguous methodological decisions during the setup of an LCI include the modeling of end-of-life scenarios or the choice of an allocation factor for the allocation of joint co-production processes. An important criterion for methodological decisions — besides the conformity with the relevant series of standards ISO 14 040 — is if the improvement options, which can be deduced from the LCI, are perceived by the decision-maker as to redirect the material flows at stake into more sustainable paths.     

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.     

LCA and decision making: when and how to use consequential LCA; 62nd LCA forum,Swiss Federal Institute of Technology,Zürich, 9 September 2016

The 62nd life cycle assessment (LCA) forum was held on 9 September 2016 to discuss the state of research and application with regard to consequential life cycle assessment. This conference report presents the highlights of the LCA forum. The state of the art of consequential LCA was presented from different viewpoints. It was pointed out that consequential LCA is more than marginal mixes and avoided burdens and involves causal modelling. It was also said that social responsibility calls for consequential LCA. Currently, different models are used to support decision making. It was suggested to make use of the variety of models to check the conclusiveness of their results and thus the reliability of the LCAs. Current and future implementations of consequential LCI models in background databases and linking algorithms were presented. Several speakers presented consequential LCA case studies covering the sectors energy, transport, housing and mining. Some of the LCA models used in the case studies are complemented with general and partial computable equilibrium models and agent-based models and use environmentally extended input-output data or process-based LCA data. Some of the presentations focused on elements such as constrained production, marginal market mixes and technologies or recycling and system expansion. In three parallel workshops, the needs, contents and methodology, and implementation of consequential LCA approaches were discussed. The participants seemed to generally agree on the basic goal that LCA should be able to reflect the consequences of decisions. The inquiry among the participants showed that the demand for consequential LCA studies is hardly existent. The appropriate implementation of consequential modelling in LCA databases and on the appropriate model to be used in consequential LCA case studies was debated. It revealed a need for further and extensive discussions to be able to reach (minimum) consensus.     

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.     

Ambiguities in decision-oriented Life Cycle Inventories The Role of mental models

If the complexity of real, socio-economic systems is acknowledged, life cycle inventory analysis (LCI) in life cycle assessment (LCA) cannot be considered as unambiguous, objective, and as an exclusively data and science based attribution of material and energy flows to a product. The paper thus suggests a set of criteria for LCI derived from different scientific disciplines, practice of product design and modelling characteristics of LCI and LCA. A product system with its respective LCI supporting the process of effective and efficient decision-making should ideally be: a) complete, operational, decomposable, non-redundant, minimal, and comparable; b) efficient, i.e., as simple, manageable, transparent, cheap, quick, but still as ‘adequate’ as possible under a functionalistic perspective which takes given economic constraints, material and market characteristics, and the goal and scope of the study into account; c) actor-based when reflecting the decision-makers’ action space, risk-level, values, and knowledge (i.e. mental model) in view of the management rules of sustainable development; d) as site- and case-specific as possible, i.e. uses as much site-specific information as possible. This rationale stresses the significance of considering both (i) material and energy flows within the technosphere with regard to the sustainable management rules; (ii) environmental consequences of the environmental interventions on ecosphere. Further, the marginal cost of collecting and computing more and better information about environmental impacts must not exceed the marginal benefits of information for the natural environment. The ratio of environmental benefits to the economic cost of the tool must be efficient compared to other investment options. As a conclusion, in comparative LCAs, the application of equal allocation procedures does not lead to LCA-results on which products made from different materials can be compared in an adequate way. Each product and material must be modelled according to its specific material and market characteristics as well as to its particular management rules for their sustainable use. A generic LCA-methodology including preferences on methodological options is not definable.     

Parameterised inventories for life cycle assessment

Background and Objective  

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

Notions on the Design and Use of an Ideal Regional or Global LCA Database

Goal, Scope and Background

More and more national and regional life cycle assessment (LCA) databases are being established satisfying the increasing demand on LCA in policy making (e.g. Integrated Product Policy, IPP) and in industry. In order to create harmonised datasets in such unified databases, a common understanding and common rules are required. This paper describes major requirements on the way towards an ideal national background LCA database in terms of co-operation, but also in terms of life cycle inventory analysis (LCI) and impact assessment (LCIA) methodology.

Methods

A classification of disputed methodological issues is made according to their consensus potential. In LCI, three main areas of dissent are identified where consensus seems hardly possible, namely system modelling (consequential versus attributional), allocation (including recycling) and reporting (transparency and progressiveness). In LCIA the time aspect is added to the well-known value judgements of the weighting step.

Results and Discussions

It is concluded that LCA methodology should rather allow for plurality than to urge harmonisation in any case. A series of questions is proposed to identify the most appropriate content of the LCA background database or the most appropriate LCI dataset. The questions help to identify the best suited approach in modelling the product system in general and multioutput and recycling processes in particular. They additionally help to clarify the position with regard to time preferences in LCIA. Intentionally, the answers to these questions are not attributed to particular goal and scope definitions, although some recommendations and clarifying explanations are provided.

Recommendations and Perspective

It is concluded that there is not one single ideal background database content. Value judgements are also present in LCI modelling and require pluralistic solutions; solutions possibly based on the same primary data. It is recommended to focus the methodological discussion on aspects where consensus is within reach, sensible and of added value for all parties.

     

Assessing agricultural soil acidification and nutrient management in life cycle assessment

Purpose  

This paper describes part of the first detailed environmental life cycle assessment (LCA) of Australian red meat (beef and sheep meat) production. The study was intended to assist the methodological development of life cycle impact assessment by examining the feasibility of new indicators for natural resource management (NRM) issues relevant to soil management in agricultural LCA. This paper is intended to describe the NRM indicators directly related to agricultural soil chemistry.     

Using fuzzy numbers to propagate uncertainty in matrix-based LCI

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

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.     

Traffic noise in LCA

Background, aim, and scope  

According to some recent studies, noise from road transport is estimated to cause human health effects of the same order of magnitude as the sum of all other emissions from the transport life cycle. Thus, ISO 14′040 implies that traffic noise effects should be considered in life cycle assessment (LCA) studies where transports might play an important role. So far, five methods for the inclusion of noise in LCA have been proposed. However, at present, none of them is implemented in any of the major life cycle inventory (LCI) databases and commonly used in LCA studies. The goal of the present paper is to define a requirement profile for a method to include traffic noise in LCA and to assess the compliance of the five existing methods with this profile. It concludes by identifying necessary cornerstones for a model for noise effects of generic road transports that meets all requirements.     

Traffic noise in LCA

Background, aim, and scope  

An inclusion of traffic noise effects could change considerably the overall results of many life cycle assessment (LCA) studies. However, at present, noise effects are usually not considered in LCA studies, mainly because the existing methods for their inclusion do not fulfill the requirement profile. Two methods proposed so far seem suitable for inclusion in generic life cycle inventory (LCI) databases, and a third allows for inter-modal comparison. The aim of this investigation is an in-depth analysis of the existing methods and the proposition of a framework for modeling road transport noise emissions in LCI in accordance to the requirement profile postulated in part 1.     

Choice of Allocations and Constructs for Attributional or Consequential Life Cycle Assessment and Input‐Output Analysis

The divide between attributional and consequential research perspectives partly overlaps with the long‐standing methodological discussions in the life cycle assessment (LCA) and input‐output analysis (IO) research communities on the choice of techniques and models for dealing with situations of coproduction. The recent harmonization of LCA allocations and IO constructs revealed a more diverse set of coproduction models than had previously been understood. This increased flexibility and transparency in inventory modeling warrants a re‐evaluation of the treatment of coproduction in analyses with attributional and consequential perspectives. In the present article, the main types of coproductions situations and of coproduction models are reviewed, along with key desirable characteristics of attributional and consequential studies. A concordance analysis leads to clear recommendations, which call for important refinements to current guidelines for both LCA/IO practitioners and database developers. We notably challenge the simple association between, on the one hand, attributional LCA and partition allocation, and on the one hand, consequential LCA and substitution modeling.     

Modeling process and material alternatives in life cycle assessments

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

Life cycle assessment of aquaculture systems—a review of methodologies

Purpose  

As capture fishery production has reached its limits and global demand for aquatic products is still increasing, aquaculture has become the world’s fastest growing animal production sector. In attempts to evaluate the environmental consequences of this rapid expansion, life cycle assessment (LCA) has become a frequently used method. The present review of current peer-reviewed literature focusing on LCA of aquaculture systems is intended to clarify the methodological choices made, identify possible data gaps, and provide recommendations for future development within this field of research. The results of this review will also serve as a start-up activity of the EU FP7 SEAT (Sustaining Ethical Aquaculture Trade) project, which aims to perform several LCA studies on aquaculture systems in Asia over the next few years.     

LCA of soybean meal

Background, Aim and Scope  Soybean meal is an important protein input to the European livestock production, with Argentina being an important supplier. The area cultivated with soybeans is still increasing globally, and so are the number of LCAs where the production of soybean meal forms part of the product chain. In recent years there has been increasing focus on how soybean production affects the environment. The purpose of the study was to estimate the environmental consequences of soybean meal consumption using a consequential LCA approach. The functional unit is ‘one kg of soybean meal produced in Argentina and delivered to Rotterdam Harbor’. Materials and Methods  Soybean meal has the co-product soybean oil. In this study, the consequential LCA method was applied, and co-product allocation was thereby avoided through system expansion. In this context, system expansion implies that the inputs and outputs are entirely ascribed to soybean meal, and the product system is subsequently expanded to include the avoided production of palm oil. Presently, the marginal vegetable oil on the world market is palm oil but, to be prepared for fluctuations in market demands, an alternative product system with rapeseed oil as the marginal vegetable oil has been established. EDIP97 (updated version 2.3) was used for LCIA and the following impact categories were included: Global warming, eutrophication, acidification, ozone depletion and photochemical smog. Results  Two soybean loops were established to demonstrate how an increased demand for soybean meal affects the palm oil and rapeseed oil production, respectively. The characterized results from LCA on soybean meal (with palm oil as marginal oil) were 721 gCO₂ eq. for global warming potential, 0.3 mg CFC11 eq. for ozone depletion potential, 3.1 g SO₂ eq. for acidification potential, −2 g NO₃ eq. for eutrophication potential and 0.4 g ethene eq. for photochemical smog potential per kg soybean meal. The average area per kg soybean meal consumed was 3.6 m²year. Attributional results, calculated by economic and mass allocation, are also presented. Normalised results show that the most dominating impact categories were: global warming, eutrophication and acidification. The ‘hot spot’ in relation to global warming, was ‘soybean cultivation’, dominated by N₂O emissions from degradation of crop residues (e.g., straw) and during biological nitrogen fixation. In relation to eutrophication and acidification, the transport of soybeans by truck is important, and sensitivity analyses showed that the acidification potential is very sensitive to the increased transport distance by truck. Discussion  The potential environmental impacts (except photochemical smog) were lower when using rapeseed oil as the marginal vegetable oil, because the avoided production of rapeseed contributes more negatively compared with the avoided production of palm oil. Identification of the marginal vegetable oil (palm oil or rapeseed oil) turned out to be important for the result, and this shows how crucial it is in consequential LCA to identify the right marginal product system (e.g., marginal vegetable oil). Conclusions  Consequential LCAs were successfully performed on soybean meal and LCA data on soybean meal are now available for consequential (or attributional) LCAs on livestock products. The study clearly shows that consequential LCAs are quite easy to handle, even though it has been necessary to include production of palm oil, rapeseed and spring barley, as these production systems are affected by the soybean oil co-product. Recommendations and Perspectives  We would appreciate it if the International Journal of Life Cycle Assessment had articles on the developments on, for example, marginal protein, marginal vegetable oil, marginal electricity (related to relevant markets), marginal heat, marginal cereals and, likewise, on metals and other basic commodities. This will not only facilitate the work with consequential LCAs, but will also increase the quality of LCAs.     

Attributional and consequential LCA of milk production

Background, aim and scope  Different ways of performing a life cycle assessment (LCA) are used to assess the environmental burden of milk production. A strong connection exists between the choice between attributional LCA (ALCA) and consequential LCA (CLCA) and the choice of how to handle co-products. Insight is needed in the effect of choice on results of environmental analyses of agricultural products, such as milk. The main goal of this study was to demonstrate and compare ALCA and CLCA of an average conventional milk production system in The Netherlands. Materials and methods  ALCA describes the pollution and resource flows within a chosen system attributed to the delivery of a specified amount of the functional unit. CLCA estimates how pollution and resource flows within a system change in response to a change in output of the functional unit. For an average Dutch conventional milk production system, an ALCA (mass and economic allocation) and a CLCA (system expansion) were performed. Impact categories included in the analyses were: land use, energy use, climate change, acidification and eutrophication. The comparison was based on four criteria: hotspot identification, comprehensibility, quality and availability of data. Results  Total environmental burdens were lower when using CLCA compared with ALCA. Major hotspots for the different impact categories when using CLCA and ALCA were similar, but other hotspots differed in contributions, order and type. As experienced by the authors, ALCA and use of co-product allocation are difficult to comprehend for a consequential practitioner, while CLCA and system expansion are difficult to comprehend for an attributional practitioner. Literature shows concentrates used within ALCA will be more understandable for a feeding expert than the feed used within CLCA. Outcomes of CLCA are more sensitive to uncertainties compared with ALCA, due to the inclusion of market prospects. The amount of data required within CLCA is similar compared with ALCA. Discussion  The main cause of these differences between ALCA and CLCA is the fact that different systems are modelled. The goal of the study or the research question to be answered defines the system under study. In general, the goal of CLCA is to assess environmental consequences of a change in demand, whereas the goal of ALCA is to assess the environmental burden of a product, assuming a status-quo situation. Nowadays, however, most LCA practitioners chose one methodology independent of their research question. Conclusions  This study showed it is possible to perform both ALCA (mass and economic allocation) and CLCA (system expansion) of milk. Choices of methodology, however, resulted in differences in: total quantitative outcomes, hotspots, degree of understanding and quality. Recommendations and perspectives  We recommend LCA practitioners to better distinguish between ALCA and CLCA in applied studies to reach a higher degree of transparency. Furthermore, we recommend LCA practitioners of different research areas to perform similar case studies to address differences between ALCA and CLCA of the specific products as the outcomes might differ from our study.     

Distance and backhaul in commodity transport modeling

Background, aims and scope  

The goal of this work is to provide methodological information for modeling distance and backhaul for commodity transport in a life cycle inventory (LCI). The scope includes a review of modeling parameters accounted for in transport unit process models and accounted for in unit process models using transported materials. Assumptions related to backhaul (or return trip) and transport distance are characterized and evaluated. A case study explores the contribution of transport and the bearing of assumptions on the life cycle of select US-produced metals.