Advances in application of machine learning to life cycle assessment: a literature review

Purpose

Life Cycle Assessment (LCA) is the process of systematically assessing impacts when there is an interaction between the environment and human activity. Machine learning (ML) with LCA methods can help contribute greatly to reducing impacts. The sheer number of input parameters and their uncertainties that contribute to the full life cycle make a broader application of ML complex and difficult to achieve. Hence a systems engineering approach should be taken to apply ML in isolation to aspects of the LCA. This study addresses the challenge of leveraging ML methods to deliver LCA solutions. The overarching hypothesis is that: LCA underpinned by ML methods and informed by dynamic data paves the way to more accurate LCA while supporting life cycle decision making.

Methods

In this study, previous research on ML for LCA were considered, and a literature review was undertaken.

Results

The results showed that ML can be a useful tool in certain aspects of the LCA. ML methods were shown to be applied efficiently in optimization scenarios in LCA. Finally, ML methods were integrated as part of existing inventory databases to streamline the LCA across many use cases.

Conclusions

The conclusions of this article summarise the characteristics of existing literature and provide suggestions for future work in limitations and gaps which were found in the literature.

     

Life cycle assessment of California processing tomato: an evaluation of the effects of evolving practices and technologies over a 10-year (2005–2015) timeframe

Purpose

California is the largest US producer of processing tomatoes, generating 96% of all domestic production and nearly 30% of global supply. Processing tomatoes are mostly processed into diced and paste products. Consumers and actors along their supply chains are increasingly interested in understanding their environmental burdens and identifying opportunities for improvements. This study applies life cycle assessment (LCA) to California diced and paste products over a 10-year timeframe to characterize current impacts and historical trends.

Methods

The LCA considers a scope from cradle-to-processing facility gate and accords with relevant Product Category Rules as published by the International EPD® System. Extensive primary data were collected for tomato cultivation for the years 2005 and 2015, and from processing facilities for 2005, 2010, and 2015 to understand the effects of evolving practices and technologies. We estimate crop and regional specific nitrous oxide and nitrate leaching emissions using a biogeochemical model, and the USES-LCA model is used to determine potential impacts from pesticide application. A suite of impact assessment categories is included based on the CML method (only global warming potential and freshwater consumption values are in the abstract).

Results and discussion

The 2015 results of the study indicate that diced tomatoes are responsible for 0.16 kg CO₂e and 71 L of freshwater per kg, and paste is responsible for 0.83 kg CO₂e and 328 L of freshwater per kg. The main opportunities for improvement include natural gas use in the greenhouse phase, energy for irrigation pumping and fertilizer type in the cultivation phase, and natural gas and electricity use in the facility processing phase. These hotspots are consistent with studies of processing tomato in other parts of the world. Evaluating trends over time showed that technological improvements in the industry had reduced life cycle impacts; for example, global warming potential decreased by 12% for paste and 26% for diced products between 2005 and 2015.

Conclusions

Trends over time show increasing efficiency at the cultivation and processing facility stages that have led to reductions in all impact categories evaluated. However, additional opportunities exist beyond efficiency improvements. Fertilizer and pesticide choice are potential opportunities for further reducing impacts. Also, the introduction of renewables in each phase of the supply chain (solar-powered irrigation pumps and onsite solar energy generation for facilities) could reduce the overall supply chain GWP₁₀₀ impacts by 9–10%.

     

An integrated environmental and cost assessment method based on LCA and LCC for automobile interior and exterior trim design scheme optimization

Purpose

This paper provided an integrated method to evaluate environmental impact and life cycle cost (LCC) of various alternative design schemes in the early design and development stages of complex mechanical product; an optimization method of product design schemes based on life cycle assessment (LCA) and LCC is proposed as a supporting design tool to achieve optimal integration of environmental impact and cost of the design.

Methods

The applied research methods include product level deconstruction model, LCA/LCC integrated analysis model, and the product design scheme optimization method. In the life cycle environmental assessment, GaBi software and CML2001 evaluation method are used to evaluate product environmental impact. In terms of product design configuration scheme optimization, the TOPSIS method is used to optimize the design schemes generated. Taking the internal and external trim of automobile as an example, the specific implementation process of the method is illustrated.

Results and discussion

The case study indicates that, when comprehensively considering the environmental impact and cost, the composite indices of the optimal and worst schemes are 0.8667 and 0.3001, respectively; their costs are ¥164.87 and ¥179.68, respectively; and the eco points of environmental impact are 14.74 and 39.78, respectively. The cost of the two schemes are not much different, but the environmental impact of the optimal scheme is only 37.1% of the worst scheme’s; When cost is the only factor to be considered, the lowest cost design scheme is about 36.7% of the maximum scheme’s cost, and the environmental impact of the lowest cost design scheme is about 1.6 times of the maximum cost scheme’s. When environmental impact is the only factor to be considered, the least environmental impact of design scheme accounts about 31.7% of the largest; the cost of design scheme with the least environmental impact accounts for about 58.1% of the largest one’s. Integrating LCA and LCC, scientific suggestions can be provided from several perspectives.

Conclusions

By considering the environmental impact and LCC, this paper proposes a method of product design scheme optimization as a supporting design tool which could evaluate the design options of the product and identify the preferred option in the early stage of product design. It is helpful to realize the sustainability of the product. In order to improve the applicability of this method, the weighting factors of environmental impact and cost could be adjusted according to the requirements of energy saving and emission reduction of different enterprises.

     

Life cycle environmental and economic assessment of industrial symbiosis networks: a review of the past decade of models and computational methods through a multi-level analysis lens

Purpose

Industrial symbiosis network (ISN) facilitation tools seek to holistically evaluate the environmental and economic performance of ISNs through life cycle assessment (LCA) and life cycle costing (LCC). ISNs have many stakeholders with diverse interests in the LCA and LCC results thus requiring multi-level analysis. The objective of this review was to examine the state-of-the-art methodologies used in LCAs and LCCs of ISNs and understand how multi-level analysis can be conducted.

Methods

The systematic literature review methodology was applied to develop a corpus of peer-reviewed LCA and LCC studies of ISNs published between 2010 and 2019 without any geographic boundary. Abstracts were reviewed to shortlist studies that conducted an LCA or LCC of an ISN with numerical results. LCA and LCC methodologies used in the shortlisted studies were collected and categorized. Each methodology was examined to understand how the foreground and background systems are represented, how waste-to-resource exchanges are analyzed, and how the results can be computed at the network, entity, and flow levels.

Results and discussion

The review yielded 42 LCA studies and 11 LCC studies of ISNs that used eight different methodologies. Process-based LCA was used in 71% of the LCA studies, whereas tiered hybrid LCA was used in 14% of the studies. Waste-to-resource exchanges in ISN scenarios were represented either through process analysis or as a black box. Fewer LCC studies that evaluate the economic performance of ISNs exist compared with LCA studies. Economic studies often evaluated financial feasibility, net present value, profitability, or payback period of specific waste-to-resource exchanges or the network overall.

Conclusions

The insights derived from this review chart future areas of research in multi-level modeling and analysis of the life cycle environmental and economic performance of ISNs. To improve the model construction and analysis process, research should be explored in developing a methodology for constructing a single model that represents multiple entities linked together by waste-to-resource exchanges and can provide LCA and LCC results for different stakeholder perspectives. The lack of LCC studies of ISNs merits the need for more research in this area at both the network and entity levels to quantify potential economic trade-offs between stakeholders. Developing a methodology for unified LCA and LCC modeling and analysis of ISNs can help ISN facilitation tool developers conduct simultaneous life cycle environmental and economic analysis of the potential symbiosis connections identified and how they contribute to the overall network.

     

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.

     

Water relations,photosynthesis, growth and water-use efficiency in tomato plants subjected to partial rootzone drying and regulated deficit irrigation

Abstract

Partial rootzone drying (PRD) and regulated deficit irrigation (RDI) are water-saving irrigation systems that have been developed to increase water-use efficiency (WUE) without significant yield reduction. In order to investigate whether a high-value horticultural crop such as tomato responded differently to RDI and PRD, we compared the physiological and growth responses of tomato plants using a split-root system. Plants were grown in a greenhouse under controlled conditions with their roots separated equally between two soil compartments. Three irrigation treatments were imposed: (i) Control, receiving an amount of water equivalent to 100% of plant transpiration; (ii) PRD, in which one compartment was watered with 50% of the amount of water supplied to the controls, allowing one-half of the root system to be exposed to dry soil and switching irrigation between sides weekly; and (iii) RDI, in which 50% of the amount of water given to the controls was supplied, half to each side of the root system. Leaf RWC and midday leaf Ψ decreased substantially in RDI-treated plants, while the PRD plants exhibited relatively higher Ψ and RWC values. Both PRD and RDI treatments reduced by about 30% the total plant dry mass compared with the control. However, plant transpiration was reduced by about 50% in both PRD and RDI, allowing a significant improvement in whole-plant WUE. Stomatal conductance (Gs) and leaf growth were also significantly reduced by PRD and RDI. These results may be related to a significant increase in xylem sap pH and leaf apoplastic pH. Generally, the photosynthetic apparatus of tomato leaves had a high resistance to restricted water availability. In fact, the decreased Gs had no major negative impact on carbon assimilation. However, V _cmax, i.e. Rubisco efficiency, was significantly decreased in RDI plants with respect to control ones. This may imply that, although the differences between the PRD and RDI treatments in our study were subtle, they may become more marked with a more prolonged and severe water deficit.     

Ways to get work done: a review and systematisation of simplification practices in the LCA literature

Purpose

Within the field of life cycle assessment (LCA), simplifications are a response to the practical restrictions in the context of a study. In the 1990s, simplifications were part of a debate on streamlining within LCA. Since then, many studies have been published on simplifying LCA but with little attention to systematise the approaches available. Also, despite being pervasive during the making of LCA studies, simplifications remain often invisible in the final results. This paper therefore reviews the literature on simplification in LCA in order to systematise the approaches found today.

Methods

A review of the LCA simplification literature was conducted. The systematic search and selection process led to a sample of 166 publications. During the review phase, the conceptual contributions to the simplification discourse were evaluated. A dataset of 163 entries was created, listing the conceptual contributions to the simplification debate. An empirically grounded analysis led to the generative development of a systematisation of simplifications according to their underlying simplifying logic.

Results and discussion

Five simplifying logics were identified: exclusion, inventory data substitution, qualitative expert judgment, standardisation and automation. Together, these simplifying logics inform 13 simplification strategies. The identified logics represent approaches to handle the complexities of product systems and expectations of the users of LCA results with the resources available to the analyst. Each simplification strategy is discussed with regard to its main applications and challenges.

Conclusions

This paper provides a first systematisation of the different simplification logics frequently applied in LCA since the original streamlining discussion. The presented terminology can help making communication about simplification more explicit and transparent, thus important for the credibility of LCA. Despite the pervasiveness of simplification in LCA, there is a relative lack of research on simplification per se, making further research describing simplification as a practice and analysing simplifications methodologically desirable.

     

The Mode of Action of Pederin,a Drug Inhibiting Protein Synthesis in Eucaryotic Organisms

Abstract

Pederin, a toxic substance isolated from the insect Paederus fuscipes, inhibits growth of Saccharomyces cerevisiae and EUE cells but not of Bacillus subtilis. Protein synthesis in vitro appears to be inhibited by the drug in preparations obtained from organisms containing 80 S ribosomes (yeast, EUE cells and rat liver) but not in those from organisms endowed with 70 S ribosomes (E. coli and B. subtilis). Pederin inhibits protein synthesis at a stage subsequent to the formation of the ternary complex between ribosomes, aminoacyl-tRNA and messenger RNA. Resistance or susceptibility to the drug appears to be a characteristic of ribosomes.     

Performance and environmental accounting of nutrient cycling models to estimate nitrogen emissions in agriculture and their sensitivity in life cycle assessment

Purpose

Several models are available in the literature to estimate agricultural emissions. From life cycle assessment (LCA) perspective, there is no standardized procedure for estimating emissions of nitrogen or other nutrients. This article aims to compare four agricultural models (PEF, SALCA, Daisy and Animo) with different complexity levels and test their suitability and sensitivity in LCA.

Methods

Required input data, obtained outputs, and main characteristics of the models are presented. Then, the performance of the models was evaluated according to their potential feasibility to be used in estimating nitrogen emissions in LCA using an adapted version of the criteria proposed by the United Nations Framework Convention on Climate Change (UNFCCC), and other relevant studies, to judge their suitability in LCA. Finally, nitrogen emissions from a case study of irrigated maize in Spain were estimated using the selected models and were tested in a full LCA to characterize the impacts.

Results and discussion

According to the set of criteria, the models scored, from best to worst: Daisy (77%), SALCA (74%), Animo (72%) and PEF (70%), being Daisy the most suitable model to LCA framework. Regarding the case study, the estimated emissions agreed to literature data for the irrigated corn crop in Spain and the Mediterranean, except N₂O emissions. The impact characterization showed differences of up to 56% for the most relevant impact categories when considering nitrogen emissions. Additionally, an overview of the models used to estimate nitrogen emissions in LCA studies showed that many models have been used, but not always in a suitable or justified manner.

Conclusions

Although mechanistic models are more laborious, mainly due to the amount of input data required, this study shows that Daisy could be a suitable model to estimate emissions when fertilizer application is relevant for the environmental study. In addition, and due to LCA urgently needing a solid methodology to estimate nitrogen emissions, mechanistic models such as Daisy could be used to estimate default values for different archetype scenarios.

     

Computer vision for LCA foreground modelling—an initial pipeline and proof of concept software,lcopt-cv

Purpose

The majority of LCA studies begin with the drawing of a process flow diagram, which then needs to be translated manually into an LCA model. This study presents an initial image processing pipeline, implemented in an open-source software package, called lcopt-cv, which can be used to identify the boxes and links in a photograph of a hand-drawn process flow diagram and automatically create an LCA foreground model.

Methods

The computer vision pipeline consists of a total of 15 steps, beginning with loading the image file and conversion to greyscale. The background is equalised, then the foreground of the image is extracted from the background using thresholding. The lines are then dilated and closed to account for drawing errors. Contours in the image are detected and simplified, and rectangles (contours with four corners) are identified from the simplified contours as ‘boxes’. Links between these boxes are identified using a flood-filling technique. Heuristic processing, based on knowledge of common practice in drawing of process flow diagrams, is then performed to more accurately identify the typology of the identified boxes and the direction of the links between them.

Results and discussion

The performance of the image processing pipeline was tested on four flow diagrams of increasing difficulty: one simple computer drawn diagram and three photographs of hand-drawn diagrams (a simple diagram, a complex diagram and a diagram with merged lines). A set of default values for the variables which define the pipeline was developed through trial and error. For the two simple flow charts, all boxes and links were identified using the default settings. The complex diagram required minor tweaks to the default values to detect all boxes and links. An ‘unstacking’ heuristic allowed the diagram with merged lines to be correctly processed. After some manual reclassification of link directions and process types, the diagrams were turned into LCA models and exported to open-source LCA software packages (lcopt and Brightway) to be verified and analysed.

Conclusions

This study demonstrates that it is possible to generate a fully functional LCA model from a picture of a flow chart. This has potentially important implications not only for LCA practitioners as a whole, but in particular for the teaching of LCA. Skipping the steep learning curve required by most LCA software packages allows teachers to focus on important LCA concepts, while participants maintain the benefits of experiential learning by doing a ‘real’ LCA.

     

Life cycle assessment of California unsweetened almond milk

Purpose

Plant-based alternatives to dairy milk have grown in popularity over the last decade. Almond milk comprises the largest share of plant-based milk in the US market and, as with so many food products, stakeholders in the supply chain are increasingly interested in understanding the environmental impacts of its production, particularly its carbon footprint and water consumption. This study undertakes a life cycle assessment (LCA) of a California unsweetened almond milk.

Methods

The scope of this LCA includes the production of almond milk in primary packaging at the factory gate. California produces all US almonds, which are grown under irrigated conditions. Spatially resolved modeling of almond cultivation and primary data collection from one almond milk supply chain were used to develop the LCA model. While the environmental indicators of greatest interest are global warming potential (GWP) and freshwater consumption (FWC), additional impact categories from US EPA’s TRACI assessment method are also calculated. Co-products are accounted for using economic allocation, but mass-based allocation and displacement are also tested to understand the effect of co-product allocation choices on results.

Results and discussion

The GWP and FWC of one 48 oz. (1.42 L) bottle of unsweetened almond milk are 0.71 kg CO₂e and 175 kg of water. A total of 0.39 kg CO₂e (or 55%) of the GWP is attributable to the almond milk, with the remainder attributable to packaging. Almond cultivation alone is responsible for 95% of the FWC (167 kg H₂O), because of irrigation water demand. Total primary energy consumption (TPE) is estimated at 14.8 MJ. The 48 oz. (1.42 L) PET bottle containing the almond milk is the single largest contributor to TPE (42%) and GWP (35%). Using recycled PET instead of virgin PET for the bottle considerably reduces all impact indicators except for eutrophication potential.

Conclusions

For the supply chain studied here, packaging choices provide the most immediate opportunities for reducing impacts related to GWP and TPE, but would not result in a significant reduction in FWC because irrigation water for almond cultivation is the dominant consumer. To provide context for interpretation, average US dairy milk appears to have about 4.5 times the GWP and 1.8 times the FWC of the studied almond milk on a volumetric basis.

     

Flow simulation-based particle swarm optimization for developing improved hemolysis models

The improvement and development of blood-contacting devices, such as mechanical circulatory support systems, is a life saving endeavor. These devices must be designed in such a way that they ensure the highest hemocompatibility. Therefore, in-silico trials (flow simulations) offer a quick and cost-effective way to analyze and optimize the hemocompatibility and performance of medical devices. In that regard, the prediction of blood trauma, such as hemolysis, is the key element to ensure the hemocompatibility of a device. But, despite decades of research related to numerical hemolysis models, their accuracy and reliability leaves much to be desired. This study proposes a novel optimization path, which is capable of improving existing models and aid in the development of future hemolysis models. First, flow simulations of three, turbulent blood flow test cases (capillary tube, FDA nozzle, FDA pump) were performed and hemolysis was numerically predicted by the widely-applied stress-based hemolysis models. Afterward, a multiple-objective particles swarm optimization (MOPSO) was performed to tie the physiological stresses of the simulated flow field to the measured hemolysis using an equivalent of over one million numerically determined hemolysis predictions. The results show that our optimization is capable of improving upon existing hemolysis models. However, it also unveils some deficiencies and limits of hemolysis prediction with stress-based models, which will need to be addressed in order to improve its reliability.

     

Optimizing green design using ant colony-based approach

Purpose

In response to the increasing concerns on the environmental conservation and energy saving, manufacturers are more aware of proving the ‘green’ performance of their products. Some qualitative eco design tools are used to support the development of greener products; however, most of these tools require subjective judgement during the evaluation processes. This paper is therefore to propose an alternative approach that is objective, systematic and efficient, by integrating the ant colony optimization (ACO) and life cycle assessment (LCA), to facilitate the decision-making process.

Methods

The proposed integrative LCA-ACO approach aims to support the simultaneous thorough evaluations of multiple design options. A sequence of options of the lowest corresponding environmental impact value can be obtained. A case application example of various design combinations is presented to demonstrate the applicability of the proposed approach.

Results and discussion

The proposed approach offers decision makers a preliminary fast-track approach for screening decisions without lengthy processes of LCA studies. This approach helps the decision makers, especially during the early design selection stages, identifying the most appropriate design combination from the environmental perspective. The proposed approach is proved a significant contribution to the field of LCA and green product design.

Conclusions

Since full-scale LCA studies require significant effort in data collection processes and experts for result interpretations, it would be time consuming and costly to conduct a full-scale LCA during early product development processes. The proposed approach offers a more convenient way for decision makers to assess multiple design options regarding the environmental considerations. The case example presented in this paper proves the practicality of the proposed approach.

     

Learning-by-doing: experience from 20 years of teaching LCA to future engineers

Purpose

In support of the sustainable development of our societies, future engineers should have elementary knowledge in sustainability assessment and use of life cycle assessment. Publications on pedagogical experience with teaching life cycle assessment (LCA) in high-level education are however scarce. Here, we describe and discuss 20 years of experience in teaching LCA at MSc level in an engineering university with the ambition to share our insights and inspire teaching of LCA as part of a university curriculum.

Methods

We detail the design of an LCA course taught at the Technical University of Denmark since 1997. The course structure relies on (i) a structured combination of theoretical teaching, practical assignments and hands-on practice on LCA case studies, and (ii) the conduct of real-life LCA case studies in collaboration with companies or other organisations. Through the semester-long duration of the course, students from different engineering backgrounds perform full-fledged LCA studies in groups, passing through two iterations—a screening LCA supporting a more targeted LCA.

Results and discussion

The course design, which relies on a learning-by-doing principle, is transparently described to inspire LCA teachers among the readers. Historical evolution and statistics about the course, including its 192 case studies run in collaboration with 105 companies and institutions, are analysed and serve as basis to discuss the benefits and challenges of its different components, such as the theory acquisition, the assignment work, the LCA software learning, the conduct of case studies, the merits of industrial collaborations and grading approaches.

Conclusions

We demonstrate the win-win situation created by the setting of the course, in which the students are actively engaged and learn efficiently how to perform an LCA while the collaborating companies often get useful insights into their analysed case studies. The course can also be an eye opener for companies unfamiliar with LCA, who get introduced to life cycle thinking and the potential benefits of LCA. We have no hesitation in recommending industries and LCA teachers to engage into such collaborations even in the fundamental teaching of LCA techniques.

     

Critical analysis of life cycle inventory datasets for organic crop production systems

Purpose

Organic agriculture (OA) has gained widespread popularity due to its view as a more sustainable method of farming. Yet OA and conventional agriculture (CA) can be found to have similar or varying environmental performance using tools such as life cycle assessment (LCA). However, the current state of LCA does not accurately reflect the effects of OA; thus the aim of the present study was to identify gaps in the inventory stage and suggest improvements.

Methods

This article presents for the first time a critical analysis of the life cycle inventory (LCI) of state-of-the-art organic crop LCIs from current and recommended LCA databases ecoinvent and AGRIBALYSE®. The effects of these limitations on LCA results were analyzed and detailed ways to improve upon them were proposed.

Results and discussion

Through this analysis, unrepresentative plant protection product (PPP) manufacturing and organic fertilizer treatment inventories were found to be the main limitations in background processes, due to either the lack of available usage statistics, exclusion from the study, or use of unrepresentative proxies. Many organic crop LCIs used synthetic pesticide or mineral fertilizer proxies, which may indirectly contain OA prohibited chemicals. The effect of using these proxies can contribute between 4–78% to resource and energy-related impact categories. In a foreground analysis, the fertilizer and PPP emission models utilized by ecoinvent and AGRIBALYSE® were not well adapted to organic-authorized inputs and used simplified modeling assumptions. These critical aspects can be transferred to respective LCAs that use this data, potentially yielding unrepresentative results for relevant categories. To improve accuracy and to contribute novel data to the scientific community, new manufacturing LCIs were created for a few of the missing PPPs, as well as recommendations for fertilizer treatment LCIs and more precise emission models for PPPs and fertilizers.

Conclusions

The findings in the present article add much needed transparency regarding the limitations of available OA LCIs, offers guidance on how to make OA LCIs more representative, allow for more accurate comparisons between conventional and OA, and help practitioners to better adapt LCA methodology to OA systems.

     

Comparative life cycle assessment of car disc brake systems—case study results and method discussion about comparative LCAs

Purpose

Two life cycle assessment (LCA) studies comparing a new low-particulate-matter-emission disc brake and a reference disc brake were presented. The purpose was to identify the difference in potential environmental impacts due to a material change in the new disc brake parts. Additionally, the validity was investigated for the simplification method of omitting identical parts in comparative LCA. This was done by comparing the results between the simplified and the full LCA model.

Methods

The two disc brakes, new disc brake and reference disc brake, were assessed according to the LCA ISO standards. The ReCiPe 2016 Midpoint (hierarchist) impact assessment method was chosen. Simplifying a comparative LCA is possible, all identical parts can be omitted, and only the ones that differ need to be assessed. In this paper, this simplification was called comparative LCA with an omission of identical parts.

Results and discussion

The comparative impacts were analysed over seventeen impact categories. The new disc brake alternative used more resources during the manufacture of one disc compared to the reference disc brake alternative. The shorter life length of the reference disc demanded a higher number of spare part discs to fulfil the same functional unit, but this impact was reduced due to material recycling. The new disc brake impacts were connected primarily to the coating and secondly to the pad manufacture and materials. The validity of the simplification method was investigated by comparing the results of the two LCA models. The impact differences were identical independent of the LCA model, and the same significant impact categories could be identified. Hence, the purpose of the study could be fulfilled, and the simplification was valid.

Conclusions

Both LCA models, simplified and full, revealed that the new disc brake had limited environmental advantages. The omission of identical parts made it more challenging to determine if an impact was significant or insignificant. The simplification seemed to be reasonable.

     

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.

     

Integrating life cycle assessment (LCA) and life cycle costing (LCC) in the early phases of aircraft structural design: an elevator case study

Purpose

The main objective of this paper is to develop a model that will combine economic and environmental assessment tools to support the composite material selection of aircraft structures in the early phases of design and application of the tool for an aircraft elevator.

Methods

An integrated life cycle cost (LCC) and life cycle assessment (LCA) methodology was used as part of the sustainable design approach for the laminate stacking sequence design. The model considered is the aircraft structure made of carbon fiber reinforce plastic prepreg and processed via hand layup-autoclave process which is the preferred method for the aircraft industry. The model was applied to a cargo aircraft elevator case study by comparing six different laminate configurations and two different carbon fiber prepreg materials across aircraft’s entire life cycle.

Results and discussion

The results show, in line with other studies using different methodologies (e.g., life cycle engineering, or LCE), that the combination of LCA with LCC is a worthwhile approach for comparing the different laminate configurations in terms of cost and environmental impact to support composite laminate stacking design by providing the best trade-off between cost and environment. Elevator LCC reduces 19% by changing the material type and applying different ply orientations. Elevator LCA score reduces 53% by selecting the optimum instead of best technical solution that minimizes the displacement. Improving the structural performance does not always lead to an increase in the cost.

     

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.