Quantifying system uncertainty of life cycle assessment based on Monte Carlo simulation

Background, aim, and scope

Many studies evaluate the results of applying different life cycle impact assessment (LCIA) methods to the same life cycle inventory (LCI) data and demonstrate that the assessment results would be different with different LICA methods used. Although the importance of uncertainty is recognized, most studies focus on individual stages of LCA, such as LCI and normalization and weighting stages of LCIA. However, an important question has not been answered in previous studies: Which part of the LCA processes will lead to the primary uncertainty? The understanding of the uncertainty contributions of each of the LCA components will facilitate the improvement of the credibility of LCA.

Methodology

A methodology is proposed to systematically analyze the uncertainties involved in the entire procedure of LCA. The Monte Carlo simulation is used to analyze the uncertainties associated with LCI, LCIA, and the normalization and weighting processes. Five LCIA methods are considered in this study, i.e., Eco-indicator 99, EDIP, EPS, IMPACT 2002+, and LIME. The uncertainty of the environmental performance for individual impact categories (e.g., global warming, ecotoxicity, acidification, eutrophication, photochemical smog, human health) is also calculated and compared. The LCA of municipal solid waste management strategies in Taiwan is used as a case study to illustrate the proposed methodology.

Results

The primary uncertainty source in the case study is the LCI stage under a given LCIA method. In comparison with various LCIA methods, EDIP has the highest uncertainty and Eco-indicator 99 the lowest uncertainty. Setting aside the uncertainty caused by LCI, the weighting step has higher uncertainty than the normalization step when Eco-indicator 99 is used. Comparing the uncertainty of various impact categories, the lowest is global warming, followed by eutrophication. Ecotoxicity, human health, and photochemical smog have higher uncertainty.

Discussion

In this case study of municipal waste management, it is confirmed that different LCIA methods would generate different assessment results. In other words, selection of LCIA methods is an important source of uncertainty. In this study, the impacts of human health, ecotoxicity, and photochemical smog can vary a lot when the uncertainties of LCI and LCIA procedures are considered. For the purpose of reducing the errors of impact estimation because of geographic differences, it is important to determine whether and which modifications of assessment of impact categories based on local conditions are necessary.

Conclusions

This study develops a methodology of systematically evaluating the uncertainties involved in the entire LCA procedure to identify the contributions of different assessment stages to the overall uncertainty. Which modifications of the assessment of impact categories are needed can be determined based on the comparison of uncertainty of impact categories.

Recommendations and perspectives

Such an assessment of the system uncertainty of LCA will facilitate the improvement of LCA. If the main source of uncertainty is the LCI stage, the researchers should focus on the data quality of the LCI data. If the primary source of uncertainty is the LCIA stage, direct application of LCIA to non-LCIA software developing nations should be avoided.     

Geographic variability of agriculture requires sector-specific uncertainty characterization

Purpose

Regionalization in life cycle assessment (LCA) has focused on spatially differentiated environmental variables for regional impact assessment models. Relatively less attention has been paid to spatial disparities in intermediate flows for life cycle inventory (LCI).

Methods

First, we compiled state-specific LCIs for four major crops in the USA and evaluated their geographic variability in the characterized results due to the differences in intermediate inputs. Second, we evaluated the consequence of choosing average or region-specific LCIs in understanding the life cycle environmental implications of land use change from cotton to corn or soybean. Finally, we analyzed the implications of our findings in characterizing the uncertainties associated with geographic variability under the conventional pedigree approach.

Results and discussion

Our results show that spatial disparities in LCI alone lead to two to fourfold differences in characterized results for most impact categories. The differences, however, increase to over an order of magnitude for freshwater ecotoxicity and human health non-cancer. Among the crops analyzed, winter wheat shows higher variability partly due to a larger difference in yield. As a result, the use of national average data derived from top corn and soybean producing states significantly underestimates the characterized impacts of corn and soybean in the states where land conversion from cotton to corn or soybean actually took place. The results also show that the conventional pedigree approach to uncertainty characterization in LCA substantially underestimates uncertainties arising from geographic variability of agriculture. Compared to the highest geometric standard deviation (GSD) value of 1.11 under the pedigree approach, the GSDs that we derived are as high as 7.1, with the median around 1.9.

Conclusions

The results highlight the importance of building regional life cycle inventory for understanding the environmental impacts of crops at the regional level. The high geographic variability of crops also indicates the need for sector-specific approaches to uncertainty characterization. Our results also suggest that the uncertainty values in the existing LCI databases might have been signficantly underestimated especially for those products with high geographic variability, demanding a cautious interpretation of the results derived from them. 

     

An integrated environment and cost assessment method based on LCA and LCC for mechanical product manufacturing

Purpose

The purpose of this study is to provide an integrated method to identify the resource consumption, environmental emission, and economic cost for mechanical product manufacturing from economic and ecological dimensions and ultimately to provide theoretical and data support of energy conservation and emission reduction for mechanical product manufacturing.

Methods

The applied research methods include environmental life cycle assessment (LCA) and life cycle cost (LCC). In life cycle environmental assessment, the inventory data are referred from Chinese Life Cycle Database and midpoint approach and EDIP2003 and CML2001 models of life cycle impact assessment (LCIA) are selected. In life cycle cost assessment, three cost categories are considered. The proposed environment and cost assessment method is based on the theory of social willingness to pay for potential environmental impacts. With the WD615 Steyr engine as a case, life cycle environment and cost are analyzed and evaluated.

Results and discussion

The case study indicates that, in different life cycle phases, the trend of cost result is generally similar to the environmental impacts; the largest proportion of cost and environmental impact happened in the two phases of “material production” and “component manufacturing” and the smallest proportion in “material transport” and “product assembly.” The environmental impact category of Chinese resource depletion potential (CRDP) accounted for the largest proportion, followed by global warming potential (GWP) and photochemical ozone creation potential (POCP), whereas the impacts of eutrophication potential (EP) and acidification potential (AP) are the smallest. The life cycle “conventional cost” accounted for almost all the highest percentage in each phase (except “material transport” phase), which is more than 80% of the total cost. The “environmental cost” and “possible cost” in each phase are relatively close, and the proportion of which is far below the “conventional cost.”

Conclusions

The proposed method enhanced the conventional LCA. The case results indicate that, in a life cycle framework, the environment and cost analysis results could support each other, and focusing on the environment and cost analysis for mechanical product manufacturing will contribute to a more comprehensive eco-efficiency assessment. Further research on the life cycle can be extended to phases of “early design,” “product use,” and “final disposal.” Other LCIA models and endpoint indicators are advocated for this environmental assessment. Environmental cost can also be further investigated, and the relevant social willingness to pay for more environmental emissions is advocated to be increased.

     

Reviewing the potential for including habitat fragmentation to improve life cycle impact assessments for land use impacts on biodiversity

Purpose

The biosphere is progressively subjected to a variety of pressures resulting from anthropogenic activities. Habitat conversion, resulting from anthropogenic land use, is considered the dominant factor driving terrestrial biodiversity loss. Hence, adequate modelling of land use impacts on biodiversity in decision-support tools, like life cycle assessment (LCA), is a priority. State-of-the-art life cycle impact assessment (LCIA) characterisation models for land use impacts on biodiversity translate natural habitat transformation and occupation into biodiversity impacts. However, the currently available models predominantly focus on total habitat loss and ignore the spatial configuration of the landscape. That is, habitat fragmentation effects are ignored in current LCIAs with the exception of one recently developed method.

Methods

Here, we review how habitat fragmentation may affect biodiversity. In addition, we investigate how land use impacts on biodiversity are currently modelled in LCIA and how missing fragmentation impacts can influence the LCIA model results. Finally, we discuss fragmentation literature to evaluate possible methods to include habitat fragmentation into advanced characterisation models.

Results and discussion

We found support in available ecological literature for the notion that habitat fragmentation is a relevant factor when assessing biodiversity loss. Moreover, there are models that capture fragmentation effects on biodiversity that have the potential to be incorporated into current LCIA characterisation models.

Conclusions and recommendations

To enhance the credibility of LCA biodiversity assessments, we suggest that available fragmentation models are adapted, expanded and subsequently incorporated into advanced LCIA characterisation models and promote further efforts to capture the remaining fragmentation effects in LCIA characterisation models.

     

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.     

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.

     

Uncertainty in LCA case study due to allocation approaches and life cycle impact assessment methods

Purpose

Uncertainty is present in many forms in life cycle assessment (LCA). However, little attention has been paid to analyze the variability that methodological choices have on LCA outcomes. To address this variability, common practice is to conduct a sensitivity analysis, which is sometimes treated only at a qualitative level. Hence, the purpose of this paper was to evaluate the uncertainty and the sensitivity in the LCA of swine production due to two methodological choices: the allocation approach and the life cycle impact assessment (LCIA) method.

Methods

We used a comparative case study of swine production to address uncertainty due to methodological choices. First, scenario variation through a sensitivity analysis of the approaches used to address the multi-functionality problem was conducted for the main processes of the system product, followed by an impact assessment using five LCIA methods at the midpoint level. The results from the sensitivity analysis were used to generate 10,000 independent simulations using the Monte Carlo method and then compared using comparison indicators in histogram graphics.

Results and discussion

Regardless of the differences between the absolute values of the LCA obtained due to the allocation approach and LCIA methods used, the overall ranking of scenarios did not change. The use of the substitution method to address the multi-functional processes in swine production showed the highest values for almost all of the impact categories, except for freshwater ecotoxicity; therefore, this method introduced the greater variations into our analysis. Regarding the variation of the LCIA method, for acidification, eutrophication, and freshwater ecotoxicity, the results were very sensitive. The uncertainty analysis with the Monte Carlo simulations showed a wide range of results and an almost equal probability of all the scenarios be the preferable option to decrease the impacts on acidification, eutrophication, and freshwater ecotoxicity. Considering the aggregate result variation across allocation approaches and LCIA methods, the uncertainty is too high to identify a statistically significant alternative.

Conclusions

The uncertainty analysis showed that performing only a sensitivity analysis could mislead the decision-maker with respect to LCA results; our analysis with the Monte Carlo simulation indicates no significant difference between the alternatives compared. Although the uncertainty in the LCA outcomes could not be decreased due to the wide range of possible results, to some extent, the uncertainty analysis can lead to a less uncertain decision-making by demonstrating the uncertainties between the compared alternatives.

     

A framework for implementing holistic and integrated life cycle sustainability assessment of regional bioeconomy

Purpose

Currently, social, environmental, and economic risks and chances of bioeconomy are becoming increasingly a subject of applied sustainability assessments. Based on life cycle assessment (LCA) methodology, life cycle sustainability assessment (LCSA) aims to combine or integrate social, environmental, and economic assessments. In order to contribute to the current early stage of LCSA development, this study seeks to identify a practical framework for integrated LCSA implementation.

Methods

We select possible indicators from existing suitable LCA and LCSA approaches as well as from the literature, and allocate them to a sustainability concept for holistic and integrated LCSA (HILCSA), based on the Sustainable Development Goals (SDGs). In order to conduct a practical implementation of HILCSA, we choose openLCA, because it offers the best current state and most future potential for application of LCSA. Therefore, not only the capabilities of the software and databases, but also the supported methods of life cycle impact assessments (LCIA) are evaluated regarding the requirements of the indicator set and goal and scope of future case studies.

Results and discussion

This study presents an overview of available indicators and LCIAs for bioeconomy sustainability assessments as well as their link to the SDGs. We provide a practical framework for HILCSA of regional bioeconomy, which includes an indicator set for regional (product and territorial) bioeconomy assessment, applicable with current software and databases, LCIA methods and methods of normalization, weighting, and aggregation. The implementation of HILCSA in openLCA allows an integrative LCSA by conducting all steps in a single framework with harmonized, aggregated, and coherent results. HILCSA is capable of a sustainability assessment in terms of planetary boundaries, provisioning system and societal needs, as well as communication of results to different stakeholders.

Conclusions

Our framework is capable of compensating some deficits of S-LCA, E-LCA, and economic assessments by integration, and shows main advantages compared to additive LCSA. HILCSA is capable of addressing 15 out of 17 SDGs. It addresses open questions and significant problems of LCSAs in terms of goal and scope, LCI, LCIA, and interpretation. Furthermore, HILCSA is the first of its kind actually applicable in an existing software environment. Regional bioeconomy sustainability assessment is bridging scales of global and regional effects and can inform stakeholders comprehensively on various impacts, hotspots, trade-offs, and synergies of regional bioeconomy. However, significant research needs in LCIAs, software, and indicator development remain.

     

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.     

Application of robust optimization in matrix-based LCI for decision making under uncertainty

Purpose

When product systems are optimized to minimize environmental impacts, uncertainty in the process data may impact optimal decisions. The purpose of this article is to propose a mathematical method for life cycle assessment (LCA) optimization that protects decisions against uncertainty at the life cycle inventory (LCI) stage.

Methods

A robust optimization approach is proposed for decision making under uncertainty in the LCI stage. The proposed approach incorporates data uncertainty into an optimization problem in which the matrix-based LCI model appears as a constraint. The level of protection against data uncertainty in the technology and intervention matrices can be controlled to reflect varying degrees of conservatism.

Results and discussion

A simple numerical example on an electricity generation product system is used to illustrate the main features of this methodology. A comparison is made between a robust optimization approach, and decision making using a Monte Carlo analysis. Challenges to implement the robust optimization approach on common uncertainty distributions found in LCA and on large product systems are discussed. Supporting source code is available for download at https://github.com/renwang/Robust_Optimization_LCI_Uncertainty.

Conclusions

A robust optimization approach for matrix-based LCI is proposed. The approach incorporates data uncertainties into an optimization framework for LCI and provides a mechanism to control the level of protection against uncertainty. The tool computes optimal decisions that protects against worst-case realizations of data uncertainty. The robust optimal solution is conservative and is able to avoid the negative consequences of uncertainty in decision making.     

LCA of the timber sector in Ghana: preliminary life cycle impact assessment (LCIA)

Purpose  

Most life cycle impact assessment (LCIA) approaches in life cycle assessment (LCA) are developed for western countries. Their LCIA approaches and characterization methodologies for different impact categories may not be necessarily relevant to African environmental conditions and particularly not for the timber sector in Ghana. This study reviews the relevance of existing impact categories and LCIA approaches, and uses the most relevant for the timber sector of Ghana.     

The Glasgow consensus on the delineation between pesticide emission inventory and impact assessment for LCA

Purpose

Pesticides are applied to agricultural fields to optimise crop yield and their global use is substantial. Their consideration in life cycle assessment (LCA) is affected by important inconsistencies between the emission inventory and impact assessment phases of LCA. A clear definition of the delineation between the product system model (life cycle inventory—LCI, technosphere) and the natural environment (life cycle impact assessment—LCIA, ecosphere) is missing and could be established via consensus building.

Methods

A workshop held in 2013 in Glasgow, UK, had the goal of establishing consensus and creating clear guidelines in the following topics: (1) boundary between emission inventory and impact characterisation model, (2) spatial dimensions and the time periods assumed for the application of substances to open agricultural fields or in greenhouses and (3) emissions to the natural environment and their potential impacts. More than 30 specialists in agrifood LCI, LCIA, risk assessment and ecotoxicology, representing industry, government and academia from 15 countries and four continents, met to discuss and reach consensus. The resulting guidelines target LCA practitioners, data (base) and characterisation method developers, and decision makers.

Results and discussion

The focus was on defining a clear interface between LCI and LCIA, capable of supporting any goal and scope requirements while avoiding double counting or exclusion of important emission flows/impacts. Consensus was reached accordingly on distinct sets of recommendations for LCI and LCIA, respectively, recommending, for example, that buffer zones should be considered as part of the crop production system and the change in yield be considered. While the spatial dimensions of the field were not fixed, the temporal boundary between dynamic LCI fate modelling and steady-state LCIA fate modelling needs to be defined.

Conclusions and recommendations

For pesticide application, the inventory should report pesticide identification, crop, mass applied per active ingredient, application method or formulation type, presence of buffer zones, location/country, application time before harvest and crop growth stage during application, adherence with Good Agricultural Practice, and whether the field is considered part of the technosphere or the ecosphere. Additionally, emission fractions to environmental media on-field and off-field should be reported. For LCIA, the directly concerned impact categories and a list of relevant fate and exposure processes were identified. Next steps were identified: (1) establishing default emission fractions to environmental media for integration into LCI databases and (2) interaction among impact model developers to extend current methods with new elements/processes mentioned in the recommendations.

     

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.

     

The formulation of life cycle impact assessment framework for Malaysia using Eco-indicator

Background, aim, and scope  

Life Cycle Assessment (LCA) is an emerging supporting tool designed to help practitioner in systematically assessing the environmental performance of selected product’s life cycle. A product’s life cycle includes the extraction of raw materials, production, and usage, and ends with waste treatment or disposal. Life cycle impact assessment (LCIA) as a part of LCA is a method used to derive the environmental burdens from selected product’s stages. LCIA is structured in classification, characterization, normalization and weighting. Presently most of the LCIA practices use European database to establish the characterization, normalization and weighting value. However, using these values for local LCA practice might not be able to reflect the actual Malaysian’s environmental scenario. The aim of this study is to create a Malaysian version of normalization and weighting value using the pollution database within Malaysia.     

Evaluation of Long-Term Impacts in LCA

When looking at a product’s life cycle, emissions and resource uses, as well as the resulting impacts, usually occur at different points in time. For instance, construction materials are often ‘stored’ in buildings for many decades before they are recycled or disposed of. The goal of the LCA Discussion Forum 22 was to present and discuss arguments pro and contra a temporally differentiated weighting of impacts. The discussion forum started with three talks that illustrated the importance of temporal aspects in LCI and LCIA. The following two presentations discussed the economical principles of discounting, the adequacy of this concept within LCA, and the ethical questions involved. After one further short presentation, three groups were formed that discussed questions about temporally-differentiated weighting, and consequences for LCI as well as LCIA (damage assessment and final weighting). The discussion forum ended with the following conclusions: (a) long-term impacts should be considered in LCA, and (b) long-term emissions should be inventoried separately from short-term emissions. There was no consensus on whether short-term and long-term impacts should be weighted equally. Some prefer to weigh short-term emissions higher, because they are considered to be closer. Consistent and approved forecasts should be used when considering future changes in environmental conditions in LCI and LCIA.     

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.

     

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.

     

Life cycle assessment of the Peruvian industrial anchoveta fleet: boundary setting in life cycle inventory analyses of complex and plural means of production

Purpose

This work has two major objectives: (1) to perform an attributional life cycle assessment (LCA) of a complex mean of production, the main Peruvian fishery targeting anchoveta (anchovy) and (2) to assess common assumptions regarding the exclusion of items from the life cycle inventory (LCI).

Methods

Data were compiled for 136 vessels of the 661 units in the fleet. The functional unit was 1 t of fresh fish delivered by a steel vessel. Our approach consisted of four steps: (1) a stratified sampling scheme based on a typology of the fleet, (2) a large and very detailed inventory on small representative samples with very limited exclusion based on conventional LCI approaches, (3) an impact assessment on this detailed LCI, followed by a boundary-refining process consisting of retention of items that contributed to the first 95 % of total impacts and (4) increasing the initial sample with a limited number of items, according to the results of (3). The life cycle impact assessment (LCIA) method mostly used was ReCiPe v1.07 associated to the ecoinvent database.

Results and discussion

Some items that are usually ignored in an LCI’s means of production have a significant impact. The use phase is the most important in terms of impacts (66 %), and within that phase, fuel consumption is the leading inventory item contributing to impacts (99 %). Provision of metals (with special attention to electric wiring which is often overlooked) during construction and maintenance, and of nylon for fishing nets, follows. The anchoveta fishery is shown to display the lowest fuel use intensity worldwide.

Conclusions

Boundary setting is crucial to avoid underestimation of environmental impacts of complex means of production. The construction, maintenance and EOL stages of the life cycle of fishing vessels have here a substantial environmental impact. Recommendations can be made to decrease the environmental impact of the fleet.