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PurposeThe 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.MethodsAn 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 discussionThe 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.

Remaining useful life (RUL) prediction of lithium‐ion batteries (LIBs) plays a very important role in the prognostics and health management (PHM). Accurately predicting RUL of batteries can maintain and replace the batteries in advance to guarantee the safety and stability of the energy storage system (ESS). A method based on improved ant lion optimization and support vector regression (IALO‐SVR) is proposed to accurately predict RUL of LIBs. The ALO algorithm easily falls into the local optimal solution, the levy flight algorithm is utilized to improve the shortcoming of the ALO algorithm. With the mathematical comparison of particle swarm optimization (PSO), differential evolution (DE), and ALO algorithms, the results indicate that the IALO algorithm has higher convergence accuracy. Experimental data simulations were performed using the battery datasets of NASA Prognostics Center of Excellence (PCoE) and the Center for Advanced Life Cycle Engineering (CALCE) to verify the proposed method. Through comparison with SVR, PSO‐LSSVM, and ALO‐SVR methods, the results indicate that the RUL prediction is more accurate based upon the IALO‐SVR method. Therefore, the proposed method can provide high prediction accuracy in battery health prognosis. Remaining useful life (RUL) prediction of lithium‐ion batteries (LIBs) plays a very important role in the prognostics and health management (PHM). Accurately predicting RUL of batteries can maintain and replace the batteries in advance to guarantee the safety and stability of the energy storage system (ESS). A method based on improved ant lion optimization and support vector regression (IALO‐SVR) is proposed to accurately predict RUL of LIBs. The ALO algorithm easily falls into the local optimal solution, the levy flight algorithm is utilized to improve the shortcoming of the ALO algorithm. With the mathematical comparison of particle swarm optimization (PSO), differential evolution (DE), and ALO algorithms, the results indicate that the IALO algorithm has higher convergence accuracy. Experimental data simulations were performed using the battery datasets of NASA Prognostics Center of Excellence (PCoE) and the Center for Advanced Life Cycle Engineering (CALCE) to verify the proposed method. Through comparison with SVR, PSO‐LSSVM, and ALO‐SVR methods, the results indicate that the RUL prediction is more accurate based upon the IALO‐SVR method. Therefore, the proposed method can provide high prediction accuracy in battery health prognosis.

Given the current climate emergency and the ambitious targets of carbon emissions reduction, retrofitting strategies on existing buildings typically include reducing energy demand, decarbonising the power supply, and addressing embodied carbon stored in materials. This latter point redefines the role of engineers in the transitions towards a sustainable construction sector, being they responsible for designing low impact, sustainable and carbon neutral solutions. A Life Cycle Structural Engineering (LCSE) approach, inspired by the principles of Life Cycle Thinking (LCT), should thus be adopted for the sustainable renovation of existing buildings. Only recently have pioneering approaches been proposed, tackling multifaceted buildings’ needs, such as those related to energy consumption as well as seismic safety, but often disregarding LCT principles. This study presents a redefinition of the concept of LCSE for sustainable construction and a comprehensive review of available methods and tools to operationalise the LCSE approach in practice, focusing on the consideration of LCT principles in the retrofitting design process, integration of seismic loss estimation and environmental impact assessment, and implementation of integrated retrofitting strategies. The greatest ambition of this work is thus to boost a paradigm shift for building engineers towards an interdisciplinary perspective in building assessment and retrofitting.

PurposeLife cycle assessment (LCA) has been used to assess freshwater-related impacts according to a new water footprint framework formalized in the ISO 14046 standard. To date, no consensus-based approach exists for applying this standard and results are not always comparable when different scarcity or stress indicators are used for characterization of impacts. This paper presents the outcome of a 2-year consensus building process by the Water Use in Life Cycle Assessment (WULCA), a working group of the UNEP-SETAC Life Cycle Initiative, on a water scarcity midpoint method for use in LCA and for water scarcity footprint assessments.MethodsIn the previous work, the question to be answered was identified and different expert workshops around the world led to three different proposals. After eliminating one proposal showing low relevance for the question to be answered, the remaining two were evaluated against four criteria: stakeholder acceptance, robustness with closed basins, main normative choice, and physical meaning.Results and discussionThe recommended method, AWARE, is based on the quantification of the relative available water remaining per area once the demand of humans and aquatic ecosystems has been met, answering the question “What is the potential to deprive another user (human or ecosystem) when consuming water in this area?” The resulting characterization factor (CF) ranges between 0.1 and 100 and can be used to calculate water scarcity footprints as defined in the ISO standard.ConclusionsAfter 8 years of development on water use impact assessment methods, and 2 years of consensus building, this method represents the state of the art of the current knowledge on how to assess potential impacts from water use in LCA, assessing both human and ecosystem users’ potential deprivation, at the midpoint level, and provides a consensus-based methodology for the calculation of a water scarcity footprint as per ISO 14046.

Purpose Building on the rhetoric question “ quo vadis? ” (literally “ Where are you going ?”), this article critically investigates the state of the art of normalisation and weighting approaches within life cycle assessment. It aims at identifying purposes, current practises, pros and cons, as well as research gaps in normalisation and weighting. Based on this information, the article wants to provide guidance to developers and practitioners. The underlying work was conducted under the umbrella of the UNEP-SETAC Life Cycle Initiative, Task Force on Cross-Cutting issues in life cycle impact assessment (LCIA). Methods The empirical work consisted in (i) an online survey to investigate the perception of the LCA community regarding the scientific quality and current practice concerning normalisation and weighting; (ii) a classification followed by systematic expert-based assessment of existing methods for normalisation and weighting according to a set of five criteria: scientific robustness, documentation, coverage, uncertainty and complexity. Results and discussion The survey results showed that normalised results and weighting scores are perceived as relevant for decision-making, but further development is needed to improve uncertainty and robustness. The classification and systematic assessment of methods allowed for the identification of specific advantages and limitations. Conclusions Based on the results, recommendations are provided to practitioners that desire to apply normalisation and weighting as well as to developers of the underlying methods.

PurposeThe main purpose of this review is to investigate the methodology of social life cycle assessment (SLCA) through its application to case studies. In addition, the following research aims to define the trends related to the SLCA by researchers and consultants. This study will help to map the current situation and to highlight the hot spots and weaknesses of the application of the SLCA theory.MethodsThe SLCA could be considered as a useful methodology to provide decision support in order to compare products and/or improve the social effects of the life cycle of a product. Furthermore, the results of the case studies analyzed may influence decision makers significantly. For this reason, a systematic literature review of case studies was carried out in which SLCA was applied in order to analyze closely the application of the stages of this methodology. In this study, the major phases of the technical framework for a SLCA were analyzed. Specific attention was paid to detect the positive impacts that emerged in the case studies, which were also studied by administering a questionnaire to the authors of the analyzed case studies and to a number of experts in the field of SLCA.Results and discussionThe 35 case studies examined in this paper, even though they do not deviate from the 40 identified by the previous processing, are still significantly different in terms of outcome produced. It is important to clarify that the authors who developed the case studies considered the steps defined in the SETAC/SETAC guidelines, borrowed from the ISO 14044 standard.ConclusionsThe data resulting from this analysis could help both practitioners and researchers to understand what the issues are, on which it is still necessary to investigate and work, in order to solidify the SLCA methodology and define its role in the context of life cycle sustainability assessment (LCSA).

PURPOSE: Life cycle impact assessment (LCIA) is a field of active development. The last decade has seen prolific publication of new impact assessment methods covering many different impact categories and providing characterization factors that often deviate from each other for the same substance and impact. The LCA standard ISO 14044 is rather general and unspecific in its requirements and offers little help to the LCA practitioner who needs to make a choice. With the aim to identify the best among existing characterization models and provide recommendations to the LCA practitioner, a study was performed for the Joint Research Centre of the European Commission (JRC). METHODS: Existing LCIA methods were collected and their individual characterization models identified at both midpoint and endpoint levels and supplemented with other environmental models of potential use for LCIA. No new developments of characterization models or factors were done in the project. From a total of 156 models, 91 were short listed as possible candidates for a recommendation within their impact category. Criteria were developed for analyzing the models within each impact category. The criteria addressed both scientific qualities and stakeholder acceptance. The criteria were reviewed by external experts and stakeholders and applied in a comprehensive analysis of the short-listed characterization models (the total number of criteria varied between 35 and 50 per impact category). For each impact category, the analysis concluded with identification of the best among the existing characterization models. If the identified model was of sufficient quality, it was recommended by the JRC. Analysis and recommendation process involved hearing of both scientific experts and stakeholders. RESULTS AND RECOMMENDATIONS: Recommendations were developed for 14 impact categories at midpoint level, and among these recommendations, three were classified as “satisfactory” while ten were “in need of some improvements” and one was so weak that it has “to be applied with caution.” For some of the impact categories, the classification of the recommended model varied with the type of substance. At endpoint level, recommendations were only found relevant for three impact categories. For the rest, the quality of the existing methods was too weak, and the methods that came out best in the analysis were classified as “interim,” i.e., not recommended by the JRC but suitable to provide an initial basis for further development. DISCUSSION, CONCLUSIONS, AND OUTLOOK: The level of characterization modeling at midpoint level has improved considerably over the last decade and now also considers important aspects like geographical differentiation and combination of midpoint and endpoint characterization, although the latter is in clear need for further development. With the realization of the potential importance of geographical differentiation comes the need for characterization models that are able to produce characterization factors that are representative for different continents and still support aggregation of impact scores over the whole life cycle. For the impact categories human toxicity and ecotoxicity, we are now able to recommend a model, but the number of chemical substances in common use is so high that there is a need to address the substance data shortage and calculate characterization factors for many new substances. Another unresolved issue is the need for quantitative information about the uncertainties that accompany the characterization factors. This is still only adequately addressed for one or two impact categories at midpoint, and this should be a focus point in future research. The dynamic character of LCIA research means that what is best practice will change quickly in time. The characterization methods presented in this paper represent what was best practice in 2008–2009.

The U.S. health care sector is highly interconnected with industrial activities that emit much of the nation's pollution to air, water, and soils. We estimate emissions directly and indirectly attributable to the health care sector, and potential harmful effects on public health. Negative environmental and public health outcomes were estimated through economic input-output life cycle assessment (EIOLCA) modeling using National Health Expenditures (NHE) for the decade 2003-2013 and compared to national totals. In 2013, the health care sector was also responsible for significant fractions of national air pollution emissions and impacts, including acid rain (12%), greenhouse gas emissions (10%), smog formation (10%) criteria air pollutants (9%), stratospheric ozone depletion (1%), and carcinogenic and non-carcinogenic air toxics (1-2%). The largest contributors to impacts are discussed from both the supply side (EIOLCA economic sectors) and demand side (NHE categories), as are trends over the study period. Health damages from these pollutants are estimated at 470,000 DALYs lost from pollution-related disease, or 405,000 DALYs when adjusted for recent shifts in power generation sector emissions. These indirect health burdens are commensurate with the 44,000-98,000 people who die in hospitals each year in the U.S. as a result of preventable medical errors, but are currently not attributed to our health system. Concerted efforts to improve environmental performance of health care could reduce expenditures directly through waste reduction and energy savings, and indirectly through reducing pollution burden on public health, and ought to be included in efforts to improve health care quality and safety.

With the rapid advancement of cyber-physical systems, Digital Twin (DT) is gaining ever-increasing attention owing to its great capabilities to realize Industry 4.0. Enterprises from different fields are taking advantage of its ability to simulate real-time working conditions and perform intelligent decision-making, where a cost-effective solution can be readily delivered to meet individual stakeholder demands. As a hot topic, many approaches have been designed and implemented to date. However, most approaches today lack a comprehensive review to examine DT benefits by considering both engineering product lifecycle management and business innovation as a whole. To fill this gap, this work conducts a state-of-the art survey of DT by selecting 123 representative items together with 22 supplementary works to address those two perspectives, while considering technical aspects as a fundamental. The systematic review further identifies eight future perspectives for DT, including modular DT, modeling consistency and accuracy, incorporation of Big Data analytics in DT models, DT simulation improvements, VR integration into DT, expansion of DT domains, efficient mapping of cyber-physical data and cloud/edge computing integration. This work sets out to be a guide to the status of DT development and application in today’s academic and industrial environment.