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.     

A hybrid life cycle assessment of water treatment chemicals: an Australian experience

Purpose

Life cycle assessment (LCA) of chemicals is usually developed using a process-based approach. In this paper, we develop a tiered hybrid LCA of water treatment chemicals combining the specificity of process data with the holistic nature of input–output analysis (IOA). We compare these results with process and input–output models for the most commonly used chemicals in the Australian water industry to identify the direct and indirect environmental impacts associated with the manufacturing of these materials.

Methods

We have improved a previous Australian hybrid LCA model by updating the environmental indicators and expanding the number of included industry sectors of the economy. We also present an alternative way to estimate the expenditure vectors to the service sectors of the economy when financial data are not available. Process-based, input–output and hybrid results were calculated for caustic soda, sodium hypochlorite, ferric chloride, aluminium sulphate, fluorosilicic acid, calcium oxide and chlorine gas. The functional unit is the same for each chemical: the production of 1 tonne in the year 2008.

Results and discussion

We have provided results for seven impact categories: global warming potential; primary energy; water use; marine, freshwater and terrestrial ecotoxicity potentials and human toxicity potential. Results are compared with previous IOA and hybrid studies. A sensitivity analysis of the results to assumed wholesale prices is included. We also present insights regarding how hybrid modelling helps to overcome the limitations of using IO- or process-based modelling individually.

Conclusions and recommendations

The advantages of using hybrid modelling have been demonstrated for water treatment chemicals by expanding the boundaries of process-based modelling and also by reducing the sensitivity of IOA to fluctuations in prices of raw materials used for the production of these industrial commodities. The development of robust hybrid life cycle inventory databases is paramount if hybrid modelling is to become a standard practice in attributional LCA.     

Perceived uncertainties of characterization in LCA: a survey

Purpose

Uncertainty analyses in life cycle assessment (LCA) literature have focused primarily on the life cycle inventory (LCI) phase, but LCA experts generally agree that the life cycle impact assessment (LCIA) phase is likely to contribute even more to the overall uncertainty of an LCA result. The magnitude of perceived uncertainties in characterization relative to that in LCI, however, has not been examined in the literature. Here, we use the pedigree approach to gauge the perceived uncertainty in the characterization phase relative to the LCI phase. In addition, we evaluate the level of approval on the pedigree approach as a means to characterize uncertainty in LCA.

Methods

Applying the Numeral Unit Spread Assessment Pedigree (NUSAP) approach to environmental risk assessment literature, we extracted the criteria for evaluating the uncertainty in the characterization phase. We used expert elicitation to identify a pool of experts and conducted a survey, to which 47 LCA practitioners from 12 countries responded. In order to reduce personal biases in perceived geometric standard deviation (GSD) values, we used two reference questions on weight and life expectancy at birth for calibration.

Results

Nearly half (49%) of respondents expressed their approval to the pedigree matrix approach as a means of characterizing uncertainties in LCA, and responses were highly sensitive to the respondent’s familiarity with the pedigree matrix. For instance, respondents who are highly familiar with the pedigree matrix were more polarized, with 15% and 19% of them expressing either strong approval or strong disapproval, respectively. Respondents less familiar with the pedigree approach were generally more favorable to its use. Compared with LCI, variability in characterization factors was influenced more strongly by geographical correlation and reliability of the underlying model, which showed 11 to 16% larger average GSDs when compared with the comparable criteria for LCI. Conversely, temporal correlation criterion was a less significant factor in characterization than in LCI.

Conclusions and discussion

Overall, survey respondents viewed LCIA characterization as only marginally more uncertain than LCI, but with a wider variability in responses on characterization than LCI. This finding indicates the need for additional research to develop more thorough methods for characterizing uncertainties in life cycle impact assessment that are compatible with the uncertainty measures in LCI.

     

Missing inventory estimation tool using extended input-output analysis

Intention, Goal, Scope, Background  Input-Output Analysis (IOA) has recently been introduced to Life Cycle Assessment (LCA). In applying IOA to LCA studies, however, it is important to note that there are both advantages and disadvantages. Objectives  This paper aims to provide a better understanding of the advantages and disadvantages of adopting IOA in LCA, and introduces the methodology and principles of the Missing Inventory Estimation Tool (MIET) as one of the approaches to combine the strengths of process-specific LCA and IOA. Additionairy, we try to identify a number of possible errors in the use of IOA for LCA purposes, due to confusion between industry output and commodity, consumer’s price and producer’s price. Method  MIET utilises the 1996 US input-output table and various environmental statistics. It is based on an explicit distinction between commodity and industry output. Results and Discussion  MIET is a self-contained, publicly available database which can be applied directly in LCA studies to estimate missing processes. Conclusion  By adopting MILT results in existing, process-based, life-cycle inventory (LCI), LCA practitioners can fully utilise the process-specific information while expanding the system boundary. Recommendations and Outlook  MIET will be continuously updated to reflect both methodological developments and newly available data sources. For supporting information sec http:// wwwJeidenuniv.nl/cml/ssp/softwarc/miet.     

环境足迹的核算与整合框架——基于生命周期评价的视角

环境足迹及其与生命周期评价(LCA)的关系是工业生态学关注的新热点。从探讨环境足迹与LCA的关系入手,以碳足迹、水足迹、土地足迹和材料足迹为例,分别对每一项足迹指标两个版本的核算方法进行了比较。根据清单加和过程的特点,将所有足迹指标划分为基于权重因子和基于特征因子两类,总结了两者的适用性和局限性。在此基础上提出了一个环境足迹核算与整合的统一框架。该框架基于LCA视角建立,但对系统边界和清单数据的要求相对灵活,因而也适用于生命周期不甚明确的情形。研究在一定程度上揭示了足迹指标的方法学实质,同时也为环境影响综合评估提供了一条规范化的途径。     

Is mainstream LCA linear?

Purpose

It is frequently mentioned in literature that LCA is linear, without a proof, or even without a clear definition of the criterion for linearity. Here we study the meaning of the term linear, and in relation to that, the question if LCA is indeed linear.

Methods

We explore the different meanings of the term linearity in the context of mathematical models. This leads to a distinction between linear functions, homogeneous functions, homogenous linear functions, bilinear functions, and multilinear functions. Each of them is defined in accessible terms and illustrated with examples.

Results

We analyze traditional, matrix-based, LCA, and conclude that LCA is not linear in any of the senses defined.

Discussion and conclusions

Despite the negative answer to the research question, there are many respects in which LCA can be regarded to be, at least to some extent, linear. We discuss a few of such cases. We also discuss a few practical implications for practitioners of LCA and for developers of new methods for LCI and LCIA.

     

Integration of working environment into life cycle assessment framework

Background, aim, and scope  Life cycle assessment (LCA) has been considered one of the tools for supporting decision-making related to the environmental aspects of a product system. It has mainly been used to evaluate the potential impacts associated with relevant inputs and outputs to/from a given product system throughout its life cycle. In most cases, LCA has not considered the impacts on the internal environment, i.e. working environment, but only the external environment. Recently, it has been recognized that the consideration of the impacts on the working environment as well as on the external environment, is needed in order to assess all aspects of the effects on human well-being. To this end, this study has developed a total environmental assessment methodology which enables one to integrate both the working environment and the external environment into the conventional LCA framework. Materials and methods  In general, the characteristics of the impacts on the external environment are different from those on the working environment. In order to properly integrate the two types into total environmental impacts, it is necessary to define identical system boundaries and select impact category indicators at the same level. In order to define the identical system boundary and reduce the uncertainties of LCI results, the hybrid IOA (input–output analysis) method, which integrates the advantages between conventional LCI method and IOA method, is introduced to collect input and output data throughout the entire life cycle of a given product. For the impact category indicators at the endpoint level, LWD (Lost Work Days) is employed to evaluate the damage to human health and safety in the working environment, while DALY (disability-adjusted life years) and PAF (Potentially Affected Fraction) are selected to evaluate the damage to human health and eco-system quality in the external environment, respectively. Results and discussion  The environmental intervention factors (EIFs) are developed not only for the data categories of resource use, air emissions, and water emissions, but also for occupational health and safety to complete a life cycle inventory table. For the development of the EIFs on occupational health and safety, in particular, the number of workers affected by i hazardous items and the number of workers affected at the i magnitude of disability are collected. For the characterization of the impact categories in the working environment, such as occupational health and safety, the exposure factors, effect factors, and damage factors are developed to calculate the LWD of each category. For normalization, the normalization reference is defined as the total LWD divided by the total number of workers. A case study is presented to illustrate the applicability of the proposed method for the integration of the working environment into the conventional LCA framework. Conclusions  This study is intended to develop a methodology which enables one to integrate the working environmental module into the conventional LCA framework. The hybrid IOA method is utilized to extend the system boundary of both the working environment module and the external environment module to the entire life cycle of a product system. In this study, characterization models and category indicators for occupational health and safety are proposed, respectively, while the methodology of Eco-indicator 99 is used for the external environment. In addition to aid further understanding on the results of this method, this study introduced and developed the category indicators such as DALY, and LWD, which can be expressed as a function of time, and introduced PAF, which can be expressed as a probability. Recommendations and perspectives  The consideration of the impacts not only on the external environment, but also on the working environment, is very important, because the best solution for the external environment may not necessarily be the best solution for the working environment. It is expected that the integration of occupational health and safety matters into the conventional LCA framework can bring many benefits to individuals, as well as industrial companies, by avoiding duplicated measures and false optimization.     

Carbon and water footprints of Brazilian mango produced in the semiarid region

Purpose

Awareness regarding carbon and water footprint has gained visibility, encouraging actions towards compliance with the main available standards by fruit producers. This study presents the carbon and water footprint of packed mango produced in Vale do São Francisco, the main irrigated valley in Brazil. It provides an approach to identify the critical processes and opportunities for improvements in the conventional crop system that may support producers in the task of developing future site-specific assessments.

Methods

This assessment followed ISO 14046 and ISO 14067 for water and carbon footprints, respectively, as well as specific requirements of product category rule (PCR) 013 for fruits and nuts and Publicly Available Specification (PAS) 2051-1 for horticulture products. Primary data was collected for nursery (seedling), land use change, crop production, and packaging, considering five exported mango varieties: Palmer, Keitt, Kent, Haden, and Tommy Atkins. The carbon footprint assessment was based on the impact category climate change, while water footprint encompassed the following categories: water scarcity, marine and freshwater eutrophication, human toxicity (carcinogenic and non-carcinogenic), and freshwater ecotoxicity. The footprint analysis was performed for 1 kg of packed mango.

Results and discussion

The three main processes responsible for both footprints were related to crop production: fertilizer and electricity production as well as mango cropping. Moving from Caatinga vegetation to mango orchards increased carbon storage but was not enough to offset the impact on climate change. For water footprint, it was observed that the total volume of applied irrigation water was already below technical requirements and cannot be reduced, the same occurring for nitrogen fertilization. Scenario analysis showed that the use of alternative electricity sources and the reuse of wastewater brought no major improvement in results. Furthermore, the choice of local or country level characterization factors for water scarcity changed results significantly. Discussions are made regarding (i) the relevance of mango footprints when compared to other irrigated fruits, (ii) possibilities for improving mango footprint performance, (iii) the need for updating product category rules for fruits, and (iv) the quality of provided inventories and results.

Conclusions

The comparison of mango footprints with previous studies of irrigated fruits showed that mango performance is similar or better than many irrigated fruits, cultivated all over the world. Moreover, footprints may be further reduced if mango orchards are established in previously deforested land or areas occupied with annual crops and if improvements are made in the irrigation and fertilization practices at each mango production stage.

     

A Structure Comparison of two Approaches to LCA Inventory Data, Based on the MIET and ETH Databases (10 pp)

Goal and Scope This study compared two different approaches to general inventory data in LCA, one involving the process-based ETH 96 database and the other an environmentally extended Input-Output table for the US, referring to MIET (Missing Inventory Estimation Tool) 2.0. The purpose of the present paper is to highlight and explain some of the differences between the two approaches, in order to give LCA practitioners a clearer idea of the advantages and limitations of using Input-Output analysis combined with process LCA. Methods The comparison was made despite substantial differences between the two approaches, through a reduction and reclassification of the ETH process technology matrix to fit the Input-Output classification scheme and by concentrating on the structure of the processes rather than their absolute values. The structure is described in terms of the percentage of the CO2 contribution to the total emission by all processes involved in the supply chain. An input and output structure comparison was carried out between ETH 96 and MIET 2.0, to extract information about their structures. Results and Discussion The results of the study show that, despite their methodological differences, MIET 2.0 and ETH 96 show substantial similarities in their overall structures. There are also differences in the structure of the two databases, and most of them have occurred randomly, while, for certain particular sectors, the differences are rather persistent. Especially the contributions by capital goods are constantly lower in ETH 96 database and vice versa. The results imply possible systematic truncation in process LCA databases, especially for a few sectors such as capital goods. Recommendation and Perspective Hybrid analysis can overcome the problem of incompleteness in process LCA, while avoiding such disadvantages of IOA as aggregation problem.     

Capital in the American carbon,energy, and material footprint

Stocks of fixed capital play a vital role in fulfilling basic human needs and facilitating industrial production. Their build‐up requires great quantities of energy and materials, and generates greenhouse gas emissions and other pollution. Capital stocks influence economic production and environmental pollution through their construction and over subsequent decades through their use. We perform an environmental footprint analysis of total consumption, capital investment, and capital consumption in the United States for 2007 and 2012. In 2012, capital consumption accounted for 13%, 19%, and 40% of total carbon, energy, and material footprints, respectively. Housing, federal defense, state and local government education and other services (including household consumption of roads), personal transport fuels, and hospitals are the consumption sectors with largest capital footprints. These sectors provide fundamental needs of shelter, transport, education, and health, underlying the importance of capital services. Endogenizing capital causes the biggest proportional increase to footprints of sectors with low environmental multipliers. This work builds upon existing input‐output models of production and consumption in the United States, and provides a capital‐inclusive database of carbon, energy, and material footprints and multipliers for 2007 and 2012. This article met the requirements for a gold – gold JIE data openness badge described at http://jie.click/badges .     

Effect of methodological choice on the estimated impacts of wool production and the significance for LCA-based rating systems

Purpose

One aim of LCA-based rating tools developed by the apparel industry is to promote a change in demand for textiles by influencing consumer preferences based on the environmental footprint of textiles. Despite a growing consensus that footprints developed using attributional LCA (aLCA) are not suitable to inform decisions that will impact supply and demand, these tools continue to use aLCA. This paper analyses the application of the LCA methods to wool production, specifically the application of aLCA methods that provide a retrospective assessment of impacts and consequential (cLCA) methods that estimate the impacts of a change.

Methods

Attributional and consequential life cycle inventories (LCIs) were developed and analysed to examine how the different methodological approaches affect the estimated environmental impacts of wool.

Results and discussion

Life cycle impact assessment (LCIA) of aLCI and cLCI for wool indicates that estimated global warming and water stress impacts may be considerably lower for additional production of wool, as estimated by cLCIA, than for current production as estimated by aLCIA. However, fossil resource impacts for additional production may be greater than for current production when increased wool production was assumed to displace dedicated sheep meat production.

Conclusions

This work supports the notion that the use of a retrospective assessment method (i.e. aLCA) to produce information that will guide consumer preferences may not adequately represent the impacts of a consumer’s choice because the difference between aLCIA and cLCIA results may be relatively large. As such, rating tools based on attributional LCA are unlikely to be an adequate indicator of the sustainability of textiles used in the apparel industry.

     

Hybrid Framework for Managing Uncertainty in Life Cycle Inventories

Life cycle assessment (LCA) is increasingly being used to inform decisions related to environmental technologies and polices, such as carbon footprinting and labeling, national emission inventories, and appliance standards. However, LCA studies of the same product or service often yield very different results, affecting the perception of LCA as a reliable decision tool. This does not imply that LCA is intrinsically unreliable; we argue instead that future development of LCA requires that much more attention be paid to assessing and managing uncertainties. In this article we review past efforts to manage uncertainty and propose a hybrid approach combining process and economic input–output (I‐O) approaches to uncertainty analysis of life cycle inventories (LCI). Different categories of uncertainty are sometimes not tractable to analysis within a given model framework but can be estimated from another perspective. For instance, cutoff or truncation error induced by some processes not being included in a bottom‐up process model can be estimated via a top‐down approach such as the economic I‐O model. A categorization of uncertainty types is presented (data, cutoff, aggregation, temporal, geographic) with a quantitative discussion of methods for evaluation, particularly for assessing temporal uncertainty. A long‐term vision for LCI is proposed in which hybrid methods are employed to quantitatively estimate different uncertainty types, which are then reduced through an iterative refinement of the hybrid LCI method.     

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.

     

Global Supply Chains of Coltan

The spot price for tantalum, a metal used in high‐performance consumer electronics, spiked in 2000, triggering a boom in artisanal mining of surface deposits in the Democratic Republic of Congo (DRC). The profit from columbite‐tantalite ore, or coltan, is alleged to have funded militants during that country's civil war. One warlord famously claimed that in 2000, coltan delivered a million dollars per month. While coltan mining was neither a necessary nor sufficient cause for the civil war, there is nevertheless a clear association between mining and conflict. In order to trace global flows of coltan out of the DRC, we used a high‐resolution multiregion input‐output (MRIO) table and a hybrid life cycle assessment (LCA) approach to trace exports through international supply chains in order to estimate a “coltan footprint” for various products. In this case study, our aim is to highlight the power and utility of hybrid LCA analysis using high‐resolution global MRIO accounts. We estimate which supply chains, nations, and consumer goods carry the largest loads of embodied coltan. This hybrid LCA case study provides estimates on illicit flows of coltan, estimates a coltan footprint of consumption, and highlights the advantages and challenges of using hybrid monetary‐physical input‐output/LCA approaches to study and quantify a negative social impact as an input to production. If successful, the hybrid LCA approach could be a useful and expedient measurement tool for understanding flows of conflict minerals embodied in supply chains.     

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.     

A framework for modelling the transport and deposition of eroded particles towards water systems in a life cycle inventory

Purpose

Topsoil erosion due to land use has been characterised as one of the most damaging problems from the perspective of soil-resource depletion, changes in soil fertility and net soil productivity and damage to aquatic ecosystems. On-site environmental damage to topsoil by water erosion has begun to be considered in Life Cycle Assessment (LCA) within the context of ecosystem services. However, a framework for modelling soil erosion by water, addressing off-site deposition in surface water systems, to support life cycle inventory (LCI) modelling is still lacking. The objectives of this paper are to conduct an overview of existing methods addressing topsoil erosion issues in LCA and to develop a framework to support LCI modelling of topsoil erosion, transport and deposition in surface water systems, to establish a procedure for assessing the environmental damage from topsoil erosion on water ecosystems.

Methods

The main features of existing methods addressing topsoil erosion issues in LCA are analysed, particularly with respect to LCI and Life Cycle Impact Assessment methodologies. An overview of nine topsoil erosion models is performed to estimate topsoil erosion by water, soil particle transport through the landscape and its in-stream deposition. The type of erosion evaluated by each of the models, as well as their applicable spatial scale, level of input data requirements and operational complexity issues are considered. The WATEM-SEDEM model is proposed as the most adequate to perform LCI erosion analysis.

Results and discussion

The definition of land use type, the area of assessment, spatial location and system boundaries are the main elements discussed. Depending on the defined system boundaries and the inherent routing network of the detached soil particles to the water systems, the solving of the multifunctionality of the system assumes particular relevance. Simplifications related to the spatial variability of the input data parameters are recommended. Finally, a sensitivity analysis is recommended to evaluate the effects of the transport capacity coefficient in the LCI results.

Conclusions

The published LCA methods focus only on the changes of soil properties due to topsoil erosion by water. This study provides a simplified framework to perform an LCI of topsoil erosion by considering off-site deposition of eroded particles in surface water systems. The widespread use of the proposed framework would require the development of LCI erosion databases. The issues of topsoil erosion impact on aquatic biodiversity, including the development of characterisation factors, are now the subject of on-going research.     

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.

     

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.     

A scalable life cycle inventory of an automotive power electronic inverter unit—part II: manufacturing processes

Purpose

A scalable life cycle inventory (LCI) model, which provides mass composition and gate-to-gate manufacturing data for a power electronic inverter unit intended for controlling electric vehicle propulsion motors, was developed. The purpose is to fill existing data gaps for life cycle assessment (LCA) of electric vehicles. The model comprises new and easy-to-use data with sufficient level of detail to enable proper component scaling and in-depth analysis of inverter units. The aim of this article (part II) is to describe the modeling of all production steps and present new datasets. Another objective is to explain the strategies for data collection, system boundaries, and how unit process datasets were made to interact properly with the scalable design model (part I).

Methods

Data for the manufacturing of the inverter unit was collected from a variety of literature, technical specifications, factory data, site visits, and expert interviews. The model represents current levels of technology and modern industrial scale production. Industry data dates back to 2012. Some older literature is referred to, but only if it was found to remain relevant. Upstream, new data has been gathered to the point where the Ecoinvent database can be used to model a full cradle-to-gate inventory. To make the LCI model easy to use, each flow crossing the system boundary is reported with a recommended linked flow to this database.

Results and discussion

The screening and modeling of manufacturing inverter units resulted in a substantial compilation of new inventory data. In close integration with the design model, which is scalable in size over a range of 20–200 kW in nominal power and 250–700 V in DC system voltage (part I), it forms a comprehensive scalable LCI model of a typical automotive power electronic inverter unit intended for traction motor control. New production data covers electroplating of gold, electro-galvanization, machining and anodizing of aluminum, ceramic substrate fabrication, direct copper bonding, photoimaging and regenerative etching, power module assembly with a two-step soldering process, and the assembly of automotive printed circuit boards.

Conclusions

Interviews with experts were found to be vital for effective data collection and the reporting of details a key to maintaining data usability over time, for reuse, rework, and criticism by other LCA practitioners.

     

Life cycle inventory dataset review criteria—a new proposal

Purpose

A review of LCA process datasets is an important element of quality assurance for databases and for other systems to provide LCA datasets. Somewhat surprisingly, a broadly accepted and applicable set of criteria for a review of LCA process datasets was lacking so far. Different LCA databases and frameworks are proposing and using different criteria for reviewing datasets. To close this gap, a set of criteria for reviewing LCA dataset has been developed within the Life Cycle Initiative.

Methods

Previous contributions to LCA dataset review have been analysed for a start, from ISO and various LCA databases. To avoid somewhat arbitrary review criteria, four basic rules are proposed which are to be fulfilled by any dataset. Further, concepts for assessing representativeness and relevance are introduced into the criteria set from established practices in statistics and materiality. To better structure the criteria and to ease their application, they are grouped into clusters. A first version of the developed review criteria was presented in two workshops with database providers and users on different levels of experience, and draft versions of the criteria were shared within the initiative. The current version of the criteria reflects feedback received from various stakeholders and has been applied and tested in a review for newly developed datasets in Brazil, Malaysia and Thailand.

Results and discussion

Overall, 14 criteria are proposed, which are organised in clusters. The clusters are goal, model, value, relevance and procedure. For several criteria, a more science-based definition and evaluation is proposed in comparison to ‘traditional’ LCA. While most of the criteria depend on the goal and scope of dataset development, a core set of criteria are seen as essential and independent from specific LCA modelling. For all the criteria, value scales are developed, typically using an ordinal scale, following the pedigree approach.

Conclusions

Review criteria for LCI datasets are now defined based on a stringent approach. They aim to be globally acceptable, considering also database interoperability and database management aspects, as well as feedback received from various stakeholders, and thus close an important gap in LCA dataset quality assurance. The criteria take many elements of already existing criteria but are the first to fully reflect the implications of the ISO data quality definition, and add new concepts for representativeness and relevance with the idea to better reflect scientific practice outside of the LCA domain. A first application in a review showed to be feasible, with a level of effort similar to applying other review criteria. Aspects not addressed yet are the review procedure and the mutual recognition of dataset reviews, and their application for a very high number of datasets.