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The Circular Economy approach aims to close the loop of materials and to reduce waste. However, relevant product data for the optimization and management of circular approaches are often missing. Stakeholders typically lack key information: Recyclers do not know which materials/compounds to expect, producers do not know enough about the recyclability of their products, and customers do not have enough information about the environmental impact of their purchases. As a solution, this paper proposes a Digital Lifecycle Passport (DLCP) that can be written and read by various stakeholders along the full product lifecycle. Based on Plattform Industrie 4.0's Asset Administration Shell, the DLCP is readable for both humans and machines. A cloud-based app (SaaS) enables all stakeholders to create and manage DLCPs. As a use case, it is demonstrated how the DLCP can improve the sorting process of electronic waste.

The Digital Product Passport (DPP) is a concept to collect and share product-related information along a product's lifecycle. The aim is to provide all stakeholders during the product lifecycle with the information they need such that a successful Circular Economy can be implemented. At the moment, several varieties of DPPs are being developed, most of them sector-specific. As of today, however, there is no common standard as to what a DPP should look like. This paper collects requirements for the DPP from different perspectives that must be fulfilled to make it broadly applicable.

A review of LCA process datasets is an important element of quality assurance for databases and for other systems to provide LCA datasets. Somewhat surprisingly, a broadly accepted and applicable set of criteria for a review of LCA process datasets was lacking so far. Different LCA databases and frameworks are proposing and using different criteria for reviewing datasets. To close this gap, a set of criteria for reviewing LCA dataset has been developed within the Life Cycle Initiative. Previous contributions to LCA dataset review have been analysed for a start, from ISO and various LCA databases. To avoid somewhat arbitrary review criteria, four basic rules are proposed which are to be fulfilled by any dataset. Further, concepts for assessing representativeness and relevance are introduced into the criteria set from established practices in statistics and materiality. To better structure the criteria and to ease their application, they are grouped into clusters. A first version of the developed review criteria was presented in two workshops with database providers and users on different levels of experience, and draft versions of the criteria were shared within the initiative. The current version of the criteria reflects feedback received from various stakeholders and has been applied and tested in a review for newly developed datasets in Brazil, Malaysia and Thailand. Overall, 14 criteria are proposed, which are organised in clusters. The clusters are goal, model, value, relevance and procedure. For several criteria, a more science-based definition and evaluation is proposed in comparison to ‘traditional’ LCA. While most of the criteria depend on the goal and scope of dataset development, a core set of criteria are seen as essential and independent from specific LCA modelling. For all the criteria, value scales are developed, typically using an ordinal scale, following the pedigree approach. Review criteria for LCI datasets are now defined based on a stringent approach. They aim to be globally acceptable, considering also database interoperability and database management aspects, as well as feedback received from various stakeholders, and thus close an important gap in LCA dataset quality assurance. The criteria take many elements of already existing criteria but are the first to fully reflect the implications of the ISO data quality definition, and add new concepts for representativeness and relevance with the idea to better reflect scientific practice outside of the LCA domain. A first application in a review showed to be feasible, with a level of effort similar to applying other review criteria. Aspects not addressed yet are the review procedure and the mutual recognition of dataset reviews, and their application for a very high number of datasets.

Access, affordability and sustainability of raw material supply chains are crucial to the sustainable development of the European Union (EU) for both society and economy. The study investigates whether and how the social life cycle assessment (S-LCA) methodology can support responsible sourcing of raw materials in Europe. The potential of social indicators already available in an S-LCA database is tested for the development of new metrics to monitor social risks in raw material industries at EU policy level. The Product Social Impact Life Cycle Assessment (PSILCA) database was identified as a data and indicators source to assess social risks in raw material industries in EU-28 and extra-EU countries. Six raw material country sectors in the scope of the European policy on raw materials were identified and aggregated among those available in PSILCA. The selection of indicators for the assessment was based on the RACER (Relevance, Acceptance, Credibility, Ease, Robustness) analysis, leading to the proposal of 9 social impact categories. An S-LCA of the selected raw material industries was, thus, performed for the EU-28 region, followed by a contribution analysis to detect direct and indirect impacts and investigate related supply chains. Finally, the social performance of raw material sectors in EU-28 was compared with that of six extra-EU countries. Considering the overall social risks in raw material industries, “Corruption”, “Fair salary”, “Health and safety” and “Freedom of association and collective bargaining” emerged as the most significant categories both in EU and extra-EU. EU-28 shows an above-average performance where the only exception is represented by the mining and quarrying sector. An investigation of the most contributing processes to social impact categories for EU-28 led to the identification of important risks originating in the supply chain and in extra-EU areas. Therefore, the S-LCA methodology confirmed the potential of a life cycle perspective to detect burdens shifting and trade-offs. However, only a limited view on the sectoral social performance could be obtained from the research due to a lack of social data. The S-LCA methodology and indicators appear appropriate to perform an initial social sustainability screening, thus enabling the identification of hotspots in raw material supply chains and the prioritization of areas of action in EU policies. Further methodological developments in the S-LCA field are necessary to make the approach proposed in the paper fully adequate to support EU policies on raw materials.

The decentralized wastewater treatment systems including collection, treatment, and recycling of treated wastewater are an efficient integrated water management solution to overcome the shortage of freshwater and to reduce pollutant emissions. In this study, life cycle assessment was performed in order to evaluate a decentralized alternative allowing the recycling of treated greywater (TGW) in a multi-family dwelling. The adopted treatment system is the moving bed biofilm reactor (MBBR). The biological treatment with MBBRs was found the main contributor to the majority of the investigated impact categories. The integration of the tertiary treatment led to an increase in the environmental load ranging between 6 and 30.4% depending on the impact category in comparison with the scenario integrating only the biological treatment. However, the final environmental profile of both scenarios presented a minor difference. Moreover, the recycling of the TGW allowed a reduction ranging between 3.9 and 17% on five impact categories compared to the no recycling scenario. It is concluded that the decentralized approach for greywater treatment and recycling is beneficial for the environment as the distribution system and the tertiary treatment didn’t increase drastically the environmental load of the plant allowing water saving. Moreover, it is recommended to use renewable energy to reduce the environmental impact associated with the energy requirement for the biological treatment which affects the global warming potential and the acidification impact categories and to substitute the polyvinylchloride pipes with polyethylene pipes to reduce emissions related to the human toxicity impact category.

Life cycle inventory (LCI) databases are commonly used in life cycle assessment (LCA) studies. They enable modern, larger case studies, make data collection more efficient, and help to establish comparability across different case studies. A database typically tries to provide one coherent and consistent modeling space, thereby allowing users to take different datasets in the appropriate database, which implies that the goal and scope of datasets in the database match the goal and scope of case studies done with the database.

The basic model of a life cycle inventory (LCI), with unit processes as smallest modeling entities, emerged already in the very early phases of life cycle assessment (LCA) method development. It is a rather simple, linear model, with a distinction between elementary flows, product flows, and waste flows. Since the early applications, this simple model proved to be very useful and allowed for various expansions. For certain issues related to LCI modeling, solutions and approaches have evolved as extensions of the basic model. Such issues and related modeling challenges include: the multifunctionality problem; the modeling of loops in product systems; the modeling of the use phase; the modeling of transport services; the consideration of time and long-term emissions in LCI; the definition of the boundary between the technosphere and biosphere; and how to address accidents, incidents, and risks. This chapter presents and explains the basic LCA model and its extensions, where some are commonly used in practice today, and some others not. Furthermore, conventions regarding the modeling of transport services, use phase and products, end of life, are presented.

This chapter explores data quality in life cycle inventory (LCI) datasets and calculation results, introduces the history, explains the relevance of data quality for life cycle assessment (LCA), and the difficulty to deal with the application-dependency of data quality. Recent data quality systems, introduced by the United States Environmental Protection Agency (US EPA) and in the course of the European “Product Environmental Footprint” (PEF) project, are elaborated in more detail. The application-dependency of data quality has led to a more refined view on data quality in a recent United Nations GLAD (Global Life Cycle Access to Data) project. GLAD distinguishes between data quality when a dataset is created and when it is used. In addition, data quality is broadened by including modeling details that are typically set differently in different application contexts. Outcomes of the GLAD project are therefore introduced in this chapter as well and it is expected that these might lead to a more comprehensive, better management of data quality for differing application contexts, as well as for creating inventory datasets.

LCA studies require a high volume of data and their quality has a direct influence on the quality of the Life Cycle Inventory (LCI) and Life Cycle Assessment (LCA) study overall. The use of LCA databases enables users to (i) reduce time, efforts, and resources for data collection and (ii) reflect supply chains they have no direct control over. On the other side, it creates the need to align own modeling of the foreground LCA study with the modeling in the database. In recent years, countries worldwide have been more and more motivated in supporting LCA studies by providing national databases that reflect their economy, energy mix, and disposal technologies. This article aims to give insights on the main needs, requirements, and challenges for the creation of an LCA database, with a special focus on national, reference databases. First, the article defines the main characteristics of LCA datasets and discusses data collection approaches. Secondly, LCA databases are defined, and the creation of LCA databases from developed datasets is addressed, including the case of national LCA databases. Finally, the existence of tools that could ease the LCA dataset and database creation process is investigated, namely the LCA Collaboration Server and the LCA Data-Machine. It is important that countries willing to create a national database are supported, for example with capacity-building workshops, by actors with a long tradition in the field, which is of mutual benefit: Countries with a long tradition in LCA will benefit from interactions with newcomers, for instance by discussing together unsolved methodological and interoperability issues; newcomers do not need to start from scratch but can benefit from gained experiences. Creating databases that provide specific data for various parts of the world supports LCA methodology and application in general, and it is not the least a chance for local LCA communities to bring in innovation into LCA, and benefit from existing experiences at the same time.

In response to a need for interoperable LCA databases of adequate quality, a review of 40 datasets from emerging economies has been conducted with UNEP support by independent LCA experts, with the purpose to encourage improvements, either prior to publication or as part of a continuous improvement cycle. We discuss the lessons learned during this reviewing process. The review effort had to be delivered in a set and limited timeline of 9 months and covered 20 datasets from Malaysia, ten from Brazil, and ten from Thailand. The developed review process consisted in, among others, (i) developing a set of review criteria for this specific effort, (ii) pairing a local sector reviewer with an international LCA expert to enhance capacity development, (iii) establishing a legal relationship via a non-disclosure agreement (NDA) between the data provider and the data reviewers, and (iv) providing a review report for each dataset that assesses the presence and magnitude of gaps or deficiencies relative to the criteria, as well as their implications for qualifying usage. The reviews provided solid recommendations for improvement to the majority of the datasets submitted by the three countries. This review exercise has been conducted in the given timeframe for the majority of the datasets. Many challenges have been faced along the way; among them agreeing on an NDA, the need for sufficient information, and the question of the confidentiality of the data. The developed set of criteria for this exercise have not been presented or discussed outside of the narrow review exercise and therefore can be seen as a starting point for further debates and improvements that should involve the broad LCA community. This hands-on exercise provided some valuable insights into the road towards global consensus and guidance on data review. Beyond this time-constrained effort, a broader discussion and consensus at international level on the review of datasets and a continuous effort in reviewing datasets is desirable. Four key aspects were identified as necessary to consider: review criteria, review process, reviewers’ selection criteria, and development of a legal basis which protects the confidentiality of the data, as well as the position of the reviewers. This last aspect is sensitive as, in LCA datasets reviews, companies and/or countries might disclose some information with high stakes regarding competition and trade.

The value chains of raw materials and semi-finished products can create both positive and negative impacts in society, local communities, consumers, and workers. Raw materials have also a strategic importance for enhancing the competitiveness of the European industry, and creating employment (EC - European Commission, 2017a). At European level, the secure and sustainable supply of raw materials from domestic sources and international markets are key objectives of the Raw Materials Initiative (EC - European Commission, 2008a). The relationship between low security of supply and poor governance in supplier countries is acknowledged and captured in the list of Critical Raw Materials for the EU (EC - European Commission, 2017b). Internationally, many of the Sustainable Development Goals launched by the United Nations in 2015 (UN General Assembly, 2015) address, directly or indirectly, the social dimension of sustainable development and, hence, are linked to the supply of raw materials, under several aspects. In the context of sustainability assessment, Life Cycle Thinking is a well-known concept. Social Life Cycle Assessment (SLCA) evaluates social and socio-economic impacts along the life cycle of products (from the raw materials extraction, processing, manufacture, use, end of life) using a mix of generic and site specific data. Studies can be focused on a specific supply chain, or they can look at different sectors in an entire economy. In this study, we used a SLCA database for assessing and comparing the social risks associated with the supply chain of raw materials sectors at the macro scale in EU, and in a set of extra-EU countries. Negative social impacts are expressed in terms of potential risk to be exposed to negative social conditions while potential positive contributions are expressed using an opportunity evaluation. The economic sectors under investigation are those producing primary raw materials and semi-finished products, both from abiotic and biotic resources. According to the Eurostat NACE classification they are defined as: mining and quarrying; manufacture of basic metals; manufacture of non-metallic mineral products; forestry and logging; manufacture of paper and paper products; manufacture of wood and of products of wood. A set of social aspects (called subcategories, or areas of concern) was selected from those available in the database, according to criteria of relevance, data quality, etc. These include health and safety; freedom of association and collective bargaining; child labour; fair salary; working time (for the stakeholders category “workers”); respect of indigenous rights and migration (for the stakeholders category “local community”); corruption (for the stakeholders category “actors in the value chain”) and contribution to economic development (for the stakeholders category “society”). While the latter is a positive impact, the others are negative impacts occurring in the value chain. The initial results of the analysis compare social risk in the European raw materials supply chain with those of six extra-EU countries, for the set of selected social aspects. The contribution analysis shows social hotspots within a supply chain, highlighting sectors and locations that are mostly contributing to social risk in a certain subcategory. Data quality and sources of uncertainty are also discussed. As a general remark from the results of the preliminary international comparison, the social performance appears to be linked to socio-economic conditions of the country where the production activity occurs. Social risk seems to reflect also the development of a country and, to some extent, its governance. Given the granularity of the data used to assess social aspects (mostly at country, or macro-sector level), specific features of raw materials sectors are likely not captured in this analysis. This macro-scale assessment provides a first-screening assessment of supply chains, which can be used for prioritizing areas for more detailed investigation and for supporting due diligence operations at macro/sectorial scales. However, it should be complemented with bottom-up analyses in order to get a better understanding of the social consequences of more specific economic activities., JRC.D.3-Land Resources

Structural engineers focus on the reduction and partial substitution of the cement in reinforced concrete structures, while other impacts such as the ozone depletion and the air acidification become secondary or are left behind. The featured life cycle assessment shows the impacts corresponding to each construction stage of an earth-retaining wall with buttresses. The contribution ratio of each input flow is analyzed. The flows are modeled as individual subsystems of the earth-retaining wall. Accordingly, concrete, landfill, machinery, formwork, steel, and transport are defined as subsystems of the construction of the infrastructure. A previous study showed that the contribution shares of impacts do not linearly vary with the height. Therefore, a functional unit is defined per wall height. Results show that the concrete almost always accounts for the largest contribution to each impact, except for the ozone depletion potential (ODP) and the photochemical oxidation of air (POCP). The use of machinery holds the biggest ODP share (30%) while the steel is the largest contributor to POCP. Once the wall height is fixed, a parameter of study was the recycling rate. A recycling rate of 70% is usually reached. The study shows a noticeable reduction of several impacts when the recycling content increases. The relationship of the impacts of each material with the amount of material used discloses research interest. However, this relationship is greatly influenced by the cement manufacturing technology and the steel recycling potential in the area.

In recent years, increased interest and actions have been taken to better understand, and mitigate, sustainability impacts of mining activities, by both industry and policy. The present work reports on a sustainability hotspots screening performed for the EU Horizon 2020 &ldquo, Integrated Mineral Technologies for More Sustainable Raw Material Supply&rdquo, (ITERAMS) project, which foresees a more efficient water recycling, tailings valorization, and minimization of environmental footprint. The focus of this paper is on social and environmental issues in mining. Different methodologies were explored, starting from a qualitative causal loop modelling. Afterwards, an environmental and social LCA screening was performed using well-accepted databases and methods, thus completing results with a literature research. The main findings related to the importance of the supply chain, the vulnerability of local communities, and the toxic emissions from tailings offer a starting point to reflect on the specific social, socio-economic, and environmental context which may influence these issues. A better understanding of the environmental and social pressures associated with mining is not only crucial to orient the sustainability assessment foreseen for the ITERAMS project, but also to contribute in terms of methodology to the challenges tackled by policy and research worldwide towards a more sustainable mining.

In the ISO standard 14040, interpretation is one of the inter-related four phases of Life Cycle Assessment (LCA). The goal and scope phase specifies the intended purpose of the study, the targeted audience, the application, and the methods and databases that are foreseen to be used in the study. The interpretation phase then needs to analyze the outcomes of the LCA in relation to the goal and scope definition.

Purpose Ecoinvent applies a method for estimation of default standard deviations for flow data from characteristics of these flows and the respective processes that are turned into uncertainty factors in a pedigree matrix, starting from qualitative assessments. The uncertainty factors are aggregated to the standard deviation. This approach allows calculating uncertainties for all flows in the ecoinvent database. In ecoinvent 2 the uncertainty factors were provided based on expert judgment, without (documented) empirical foundation. This paper presents (1) a procedure to obtain an empirical foundation for the uncertainty factors that are used in the pedigree approach and (2) a proposal for new uncertainty factors, received by applying the developed procedure. Both the factors and the procedure are a result of a first phase of an ecoinvent project to refine the pedigree matrix approach. A separate paper in the same edition, also the result of the aforementioned project, deals with extending the developed approach to other probability distributions than lognormal (Muller et al.).

Data used in life cycle inventories are uncertain (Ciroth et al. Int J Life Cycle Assess 9(4):216–226, 2004). The ecoinvent LCI database considers uncertainty on exchange values. The default approach applied to quantify uncertainty in ecoinvent is a semi-quantitative approach based on the use of a pedigree matrix; it considers two types of uncertainties: the basic uncertainty (the epistemic error) and the additional uncertainty (the uncertainty due to using imperfect data). This approach as implemented in ecoinvent v2 has several weaknesses or limitations, one being that uncertainty is always considered as following a lognormal distribution. The aim of this paper is to show how ecoinvent v3 will apply this approach to all types of distributions allowed by the ecoSpold v2 data format. A new methodology was developed to apply the semi-quantitative approach to distributions other than the lognormal. This methodology and the consequent formulas were based on (1) how the basic and the additional uncertainties are combined for the lognormal distribution and on (2) the links between the lognormal and the normal distributions. These two points are summarized in four principles. In order to test the robustness of the proposed approach, the resulting parameters for all probability density functions (PDFs) are tested with those obtained through a Monte Carlo simulation. This comparison will validate the proposed approach. In order to combine the basic and the additional uncertainties for the considered distributions, the coefficient of variation (CV) is used as a relative measure of dispersion. Formulas to express the definition parameters for each distribution modeling a flow with its total uncertainty are given. The obtained results are illustrated with default values; they agree with the results obtained through the Monte Carlo simulation. Some limitations of the proposed approach are cited. Providing formulas to apply the semi-quantitative pedigree approach to distributions other than the lognormal will allow the life cycle assessment (LCA) practitioner to select the appropriate distribution to model a datum with its total uncertainty. These data variability definition technique can be applied on all flow exchanges and also on parameters which play an important role in ecoinvent v3.

Purpose In this two-part paper (Background and Initial Assumptions (Part 1) and Results of Survey Research (Part 2)), we present surveys whose main objective is to determine, whether and to what extent the life cycle assessment (LCA) technique is used for the identification and assessment of environmental aspects in environmental management systems (EMS) and whether there are any differences in this respect between the companies and countries analysed. Methods The survey research was carried out using the computer assisted self-administered interviewing (CASI) method among selected Polish, German and Swedish organisations which implement EMS in accordance with the requirements of ISO 14001 and/or the EMAS regulation. Results The organisations investigated, regardless of their country, are dominated by qualitative and semi-quantitative techniques of assessment and identification of environmental aspects. LCA was used sporadically, although some differences can be observed between the countries analysed. Conclusions The environmental managers accustomed to traditional qualitative and semi-quantitative solutions, have not been given preparation to enable them to understand and adopt the different approaches such as LCA. On the other hand, representatives of the organisations investigated declared that they were ready to accept an even longer timescale for the identification and assessment processes relating to environmental aspects, which represents a potential opportunity for LCA. The more precise understanding and definition of environmental problems that are precisely defined in LCA would represent a novelty for environmental managers. In practice, environmental problems are defined in a general sense and rather ambiguously, as this level of detail is sufficient in the context of qualitative and semi-quantitative techniques commonly used for the identification and assessment of environmental aspects.

Purpose In this two-part paper (Background and Initial Assumptions (part 1) and Results of Survey Research (part 2)), we present surveys whose main objective is to determine whether, and to what extent, the life cycle assessment (LCA) technique is used for the identification and assessment of environmental aspects in environmental management systems (EMS) and whether there are any differences in this respect between the companies and countries analysed. Methods The survey research was carried out using the computer assisted self-administered interviewing method among selected Polish, German and Swedish organisations which implement EMS in accordance with the requirements of ISO 14001 and/or the EMAS regulation. Results The organisations investigated, regardless of their country, are dominated by qualitative and semi-quantitative techniques of assessment and identification of environmental aspects. LCA was used sporadically, although some differences can be observed between the countries analysed. Conclusions The environmental managers accustomed to traditional qualitative and semi-quantitative solutions have not been given preparation to enable them to understand and adopt different approaches such as LCA. On the other hand, representatives of the organisations investigated declared that they were ready to accept an even longer timescale for the identification and assessment processes relating to environmental aspects, which represents a potential opportunity for LCA. The more precise understanding and definition of environmental problems that are precisely defined in LCAwould represent a novelty for environmental managers. In practice, environmental problems are defined in a general sense and rather ambiguously, as this level of detail is sufficient in the context of qualitative and semi-quantitative techniques commonly used for the identification and assessment of environmental aspects.

Life cycle costing (LCC) is applied as an assessment tool to estimate the entire cost of typically large-scale assets, for example, buildings and infrastructure objects with a considerably long serviceable life. LCC needs to consider and accumulate all types of cost, and since these occur at different times they are typically discounted to a common point in time. Environmental LCC is a specific type of LCC, which was designed to be aligned with the ISO 14040 standard for Life Cycle Assessment (LCA) and shares some key components with Environmental LCA to allow a consistent combination of both in one assessment. Societal LCC is another type of LCC, which includes externalities and attempts to provide a holistic assessment, thereby covering both economic and environmental aspects. For renewables, aspects such as feed-in tariffs and subsidies need to be considered in an LCC model. A case study for a combined heat and power plant is used to illustrate the application of Environmental LCC.

Purpose In May 2009, the Guidelines for Social Life Cycle Assessment of Products (the Guidelines) were launched at the occasion of the International Organization for Standardization (ISO) 26000 (Social Responsibility) meeting in Quebec City, Canada. Developed by a United Nations Environment Programme/Society of Environmental Toxicology and Chemistry (“UNEP/SETAC”) Life Cycle Initiative project group on Social Life Cycle Assessment (S-LCA), the Guidelines provide a framework to assess social impacts across product life cycles. A year later, the Methodological Sheets for the Subcategories of Social LCA (“the Methodological Sheets”) are being made available to support practitioners engaging in the field. The Methodological Sheets provide practical guidance for conducting S-LCA case studies by offering consistent, yet flexible assistance.

There is a need to assess social impacts of products along the full life cycle, not only to be able to address the “social dimension” in sustainability, but also for potentially improving the circumstances of affected stakeholders. This paper presents a case study for a social life cycle assessment (S-LCA) based on the recently published “Guidelines for Social Life Cycle Assessment of Products” developed by the United Nations Environment Programme/Society of Environmental Toxicology and Chemistry (UNEP/SETAC) working group. General aim is to “try out” the proposed method. The case study itself compares the impacts of rose production in Ecuador with the Netherlands. Furthermore, the objective is to identify differences and similarities in environmental and social life cycle modelling and both social and environmental hot spots in each of the life cycles. The study considers the production of rose blossoms and the cutting and packaging process in two fictitious companies in Ecuador and the Netherlands. Both rose bouquets are delivered to the European market and auctioned in Aalsmeer, the Netherlands. The social assessment is based on the UNEP/SETAC guidelines for S-LCA. Data are mainly obtained from governmental and non-governmental organisations. For the calculation of the environmental burden, a screening-type LCA is conducted, including midpoint impact assessment. This paper asserts that rose production in Ecuador is associated with many negative social effects, e.g. child labour, unfair salary, or bad impairment to health. The rose production in the Netherlands has no obvious negative social impacts but rather ecological consequences. Responsible for this is the high-energy consumption of the greenhouses. Application of the UNEP/SETAC guidelines in case studies can be encouraged based on results of this case study. The consideration of different stakeholder groups with corresponding, very diverse themes allows a comprehensive analysis of the actual conditions. However, finding suitable indicators to measure the status of the subcategories may be challenging. Moreover, the case study shows that results can be completely different for the environmental and for the social dimension, so that it often will be needed to perform both assessments if a complete picture is required. It will be interesting to apply the UNEP/SETAC approach of S-LCA to other products; products with a more complex life cycle will be a special challenge. As with any new method, getting experience on data collection and evaluation, building a data stock, integrating the method in software, and finding ways for effective communication of results are important steps until integrating S-LCA in routine, recognized decision support.

The paper presents a discussion on the possibilities of using LCA in identification and assessment of environmental aspects in environmental management systems based on the requirements of the international ISO14001 standard and the European Union EMAS regulation. Some modifications of LCA methodology are proposed in Part 1 while the results of a review of environmental aspects for 36 organisations with implemented EMS are presented in Part 2 of the article. The scope of the systems analysed in EMS and in LCA is different. This comes as the result of the fact that both ISO 14001 and EMAS are focused on an organisation on contrary to ISO14040x which are focused on a product life cycle. For the present work, this resulted in a need of adjusting the LCA methodology to EMS specificity and vice versa. Some suggestions of such modifications are presented and discussed in the paper. A preliminary analysis was carried out on 36 organisations which have environmental management systems compliant with the ISO14001 or EMAS regulations. A certain disproportion between input and output related environmental aspects included in most of the analysed registers was found. The probable reasons for such disproportion could be the fact that the output related environmental aspects are easier to manage by organisation and are often regulated by laws. Legal requirements are a significant criterion in the environmental aspects assessment. Based on the assessments carried out and the observations made, some conclusions have been drawn with regard to weaknesses and strengths and usefulness of LCA, as a result of a comparison to the traditional approaches used in EMS in the discussed area. LCA has evident advantages like: standardised methodology, possibility of inclusion of the quantitative information, presence of some methodological steps enabling the verification of the collected data, and ability to generate of reproducible results. At the same time, the following potential weak points can be observed: a complexity of the procedure, higher time, and cost requirements (especially related to an inventory phase); difficulties with assessing of environmental aspects with the qualitative character and these related to emergency situations; and limitation related to the lack of relevant characterisation factors in the currently used life cycle impact assessment methods. LCA ought to be considered as a tool used for identification and assessment of environmental aspects in environmental management systems. The listed limitations do not disqualify its suitability to be used. After certain simplifications, LCA seems to be a valuable alternative to the methodologies currently in use.

Purpose Authors of different sustainability journals, including authors of articles in past issues of the International Journal of Life Cycle Assessment have acknowledged the rising interest and th ...

Quite often there is need for precise and representative parameters in LCA studies. Probably the most relevant have direct influence on the functional unit, whose definition is crucial in the conduct of any LCA. Changes in the functional unit show directly in LCI and LCIA results. In comparative assertions, a bias in the functional unit may lead to a bias in the overall conclusions. Since quantitative data for the functional unit, such as geometric dimensions and specific weight, often vary, the question arises how to determine the functional unit, especially if a comparative assertion shall be representative for a region or market. Aim and scope of the study is to develop and apply methods for obtaining precise and representative estimates for the functional unit as one important parameter in an LCA study. Statistical sampling is applied in order to get empirical estimates for the weight of yoghurt cups, as a typical parameter for the functional unit. We used a two-stage sampling design, with stratified sampling in the first stage and three different sampling designs in the second stage, namely stratified, clustered, and a posteriori sampling. Sampling designs are motivated and described. In a case study, they are each used to determined a representative weight for 150 g yoghurt cups in Berlin, at the point of sale and within a specific time. In the first sampling stage, food markets are randomly selected, while in the second stage, yoghurt cups in these food markets are sampled. The sampling methods are applicable due to newly available internet data. These data sources and their shortcomings are described. The random sampling procedure yields representative estimates, which are compared to figures for market leaders, i.e. yoghurt cups with very high occurrence in the supermarkets. While single types of yoghurt cups showed moderate uncertainty, representative estimates were highly precise. Discussion results show, for one, the performance of the applied statistical estimation procedures, and they show further that adding more information in the estimation procedure (on the shape of the cup, on the type of plastic, on the specific brand) helps reducing uncertainty. As conclusions, estimates and their uncertainty depend on the measurement procedure in a sensitive manner; any uncertainty information should be coupled with information on the measurement procedure, and it is recommended to use statistical sampling in order to reduce uncertainty for important parameters of an LCA study. Results for market leaders differed considerably from representative estimates. This implies to not use market leader data, or data with a high market share, as substitute for representative data in LCA studies. Statistical sampling has been barely used for Life Cycle Assessment. It turned out to be a feasible means for obtaining highly precise and representative estimates for the weight of yoghurt cups in the case study, based on empirical analysis. Further research is recommended in order to detect which parameters should best be investigated in LCA case studies; which data sources are available and recommended, and which sampling designs are appropriate for different application cases.

1 IntroductionSociety of Environmental Toxicology and Chemistry(SETAC) has published a code of practice for environmentallife-cycle costing (LCC), which provides a framework forevaluating decisions with consistent, but flexible systemsboundaries as a component of product sustainabilityassessments (Swarr et al. 2011). The code of practicebuilds on an earlier monograph that summarized 3 years ofeffort by the SETAC-Europe Working Group on Life-Cycle Costing (Hunkeler et al.2008).Thecodeofpracticeis grounded in a conceptual framework for life-cyclesustainability assessment (LCSA) of products that usesdistinct analyses for each of the three pillars of sustainability,environment, economy, and social equity.LCSA ¼ LCAþ LCCþ SLCA ð1ÞLife-cycle assessment (LCA) is the only pillar that has beenstandardized to date (ISO 2006a, b). UNEP (2009)haspublished guidelines for social LCAs and is currentlydeveloping methodological sheets for impact subcategories.The code of practice reviews historical development of life-cycle methods, outlines the technical requirements and guide-lines for LCC, and illustrates various methodological choiceswith a detailed case study. The objective of the code of practiceis to provide readers with a solid understanding of how to applyLCC in parallel with LCA to stimulate additional case studiesand peer-reviewed research to further refine the methodology.The ultimate goal is to build consensus for an internationalstandard that parallels the ISO 14040 standard for LCA.2 DiscussionLCC predates LCA, and distinct and different conceptualfoundations and methodological approaches can betraced to its developmental roots in systems engineering(Blanchard 1978). There has been limited integration ofthese methods, although the value of LCC for sustainabilityassessmentshasbeenrecognized(Norris2001;Hunkelerand

In a recent letter to the editor, Jorgensen et al. questioned that life cycle costing (LCC) is relevant in life cycle-based sustainability assessment (LCSA). They hold the opinion that environmental and social aspects are sufficient. We argue that sustainability has three dimensions: environment, economy, and social aspects in accordance with the well-accepted “three pillar interpretation” of sustainability, although this is not verbally stated in the Brundtland report (WCED 1987). An analysis of the historical development of the term “sustainability” shows that the economic and social component have been present from the beginning and conclude that LCSA of product systems can be approximated by LCSA = (environmental) LCA + (environmental) LCC + S-LCA where S-LCA stands for social LCA. The “environmental” LCC is fully compatible with life cycle assessment (LCA), the internationally standardized (ISO 14040 + 14044) method for environmental product assessment. For LCC, a SETAC “Code of Practice” is now available and guidelines for S-LCA have been published by UNEP/SETAC. First examples for the use of these guidelines have been published. An important practical argument for using LCC from the customers’ point of view is that environmentally preferable products often have higher purchasing costs, whereas the LCC may be much lower (examples: energy saving light bulbs, low energy houses, and cars). Also, since LCC allows an assessment for different actor perspectives, the producers may try to keep the total costs from their perspective below those of a conventional product: otherwise, it will not succeed at the market, unless highly subsidized. Those are practical aspects whichfinally decide about success or failure of “sustainable” products. Whether or not an analysis using all three aspects is necessary will depend on the exact question. However, if real money flows are important in sustainability analysis of product systems, inclusion of LCC is advisable.

Das Vorhaben BioEnergieDat zielt auf die Bereitstellung einer aktuellen und umfassenden Datenbasis für die energetische Nutzung von Biomasse. Diese soll Informationen über den Stand der Technik, die ökologischen Effekte sowie ökonomische Kenngrößen von Produktions-, Bereitstellungs- und Konversionsprozessen enthalten. Sie ist nutzbar für die modular aufgebaute Bilanzierung von Prozessketten in einer im Vorhaben entwickelten öffentlich zugänglichen Open Source Software. Laufzeit: September 2010 - August 2012. Das Vorhaben besteht aus folgenden Teilaufgaben: Bereitstellung einer validen, konsistenten Datenbasis auf Grundlage existierender Daten zu Technologien der energetischen Nutzung von Biomasse; Erweiterung der vorliegenden Datenbasis, insbesondere im Hinblick auf in Entwicklung befindliche Technologien; Erarbeitung von unter deutschen Rahmenbedingungen repräsentativen Datensätzen für Technologien und biomassebasierte Energieträger; Bereitstellung einer webbasierten IT-Infrastruktur für eine flexible, modulare Nutzung und Fortschreibung der Datenbasis. Die Ergebnisse des Vorhabens sollen insbesondere die Entwicklung einer nachhaltigen und tragfähigen Biomassestrategie der Bundesregierung unterstützen. Sie leisten insofern auch einen Beitrag, die energiewirtschaftliche, stoffwirtschaftliche und ernährungswirtschaftliche Nutzung von Biomasse als komplexes Gesamtproblem zu behandeln. Die Bearbeitung erfolgt in einem Konsortium aus

Starting from the observation that externalities, reflecting societal concerns, emerge from costs and benefits which are not reflected in the market price, the authors of the paper emphasize the importance in education of life cycle sustainability assessment (LCSA) as a triple-bottom line tool to assess the three dimensions of sustainable development (environment, social and economy) – often referred to as the inclusive 3 P’s-approach (planet, people and profit) – of products, from cradle to grave. Especially the social LCA, as part of the overarching LCSA, has been developed to identify and to assess the social conditions throughout the life cycle of a product in order to improve human well-being. The concept of ‘social justice’ and its operationalization form the background for the development of different stakeholder categories, subcategories and indicators to undertake the social and socio-economic assessment. Two international publications (Benoît and Mazijn, 2009; Valdivia et al., 2011) are used during teaching and training session to give an overview of the social LCA and the LCSA. These guidance for the assessment of products resulted from inter- and multidisciplinary work. It was developed with the support of the authors, who have all an engineering background, but who worked for ten years now together, inter alia, with experts from social sciences. Different training sessions have been set up and LCSA (incl. social LCA) has been part of courses at universities, all with multiple objectives of a learning curve for engineering education within the context of sustainable development. Based on that experience in different countries, the authors are formulating recommendations for future educational material. Looking back at the Declaration of Barcelona (EESD 2004) and comparing with the objectives of the formal and non-formal education on LCSA, the authors claim that LCSA (and the on-going research) provides an excellent opportunity to fulfil the requirements of Engineering Education for Sustainable Development. Answering the question ‘What is a sustainable product?’ by using LCSA is learning to deal with complexity and uncertainty across the boundaries of a diversity of disciplines.

Part of: Seliger, Günther (Ed.): Innovative solutions : proceedings / 11th Global Conference on Sustainable Manufacturing, Berlin, Germany, 23rd - 25th September, 2013. - Berlin: Universitätsverlag der TU Berlin, 2013. - ISBN 978-3-7983-2609-5 (online). - http://nbn-resolving.de/urn:nbn:de:kobv:83-opus4-40276. - pp. 210–212. Integrating Product Data Management (PDM) solutions with Life Cycle Assessment (LCA) software offers the opportunity to obtain LCA results fast, based on high-quality, product-specific information and integrated into the design workflow, enabling thereby, inter alia, efficient Design for Environment (DfE). In a recent project, Dassault Systèmes and GreenDelta have investigated different options for combining LCA tools and information with the ENOVIA platform, a broadly used PDM and Product Life Cycle Management (PLM) platform by Dassault Systèmes. In the course of the project, solutions have been developed for main LCA software systems, including SimaPro, GaBi, EIME, and openLCA. A demonstration implementation has been performed for the openLCA software. A specific connector interface, called ‘eLCA’, was developed in the project; it provides an interface which makes it easy for LCA software to “dock” to eLCA that in turn links to the ENOVIA platform. The paper will describe the technical solution that has been developed and show its benefit and further potential.

Uncertainty is commonly not taken into account in LCA studies, which downgrades their usability for decision support. One often stated reason is a lack of method. The aim of this paper is to develop a method for calculating the uncertainty propagation in LCAs in a fast and reliable manner. The method is developed in a model that reflects the calculation of an LCA. For calculating the uncertainty, the model combines approximation formulas and Monte Carlo Simulation. It is based on virtual data that distinguishes true values and random errors or uncertainty, and that hence allows one to compare the performance of error propagation formulas and simulation results. The model is developed for a linear chain of processes, but extensions for covering also branched and looped product systems are made and described. The paper proposes a combined use of approximation formulas and Monte Carlo simulation for calculating uncertainty in LCAs, developed primarily for the sequential approach. During the calculation, a parameter observation controls the performance of the approximation formulas. Quantitative threshold values are given in the paper. The combination thus transcends drawbacks of simulation and approximation. The uncertainty question is a true jigsaw puzzle for LCAs and the method presented in this paper may serve as one piece in solving it. It may thus foster a sound use of uncertainty assessment in LCAs. Analysing a proper management of the input uncertainty, taking into account suitable sampling and estimation techniques; using the approach for real case studies, implementing it in LCA software for automatically applying the proposed combined uncertainty model and, on the other hand, investigating about how people do decide, and should decide, when their decision relies on explicitly uncertain LCA outcomes-these all are neighbouring puzzle pieces inviting to further work.

Qualitative valuation methods carefully try to avoid an aggregation across impact categories. However, such an aggregation often helps in obtaining a clear result for the valuation (which product scores better?). This article presents a new valuation method that uses an iterative approach. The application is demonstrated by the help of a case study for electric motors in trains. The approach combines two existing, unique valuation methods described earlier in literature, which both are of a rather non-aggregating nature, in line with ISO requirements, and were designed to be performed by LCA experts. The method is implemented in a computer software. Besides constants used within the method, the software needs as input solely indicator values from the Impact Assessment. The iterative nature of these methods itself, and especially the combination of these methods, helps in achieving a valuation result for the LCA with not more subjective and aggregating elements than necessary. Subjective elements are clearly separated from others. The algorithm seems highly sensitive to changes in impact categories regarded as important ones. The implementation in software greatly eases the application of the method by transferring routine work from LCA experts to a machine. It ensures a reproducible result and prevents erroneous steps in a rather complicated valuation procedure. It further helps in hiding the complexity of the method from the user. The approach of combining valuation methods in LCAs seems a fruitful one, and shows benefits when implemented in computer software, in terms of usability, and in terms of a more reproducible application. Care has to be taken to make sure users know what they do when performing an automated valuation procedure. We see three ways for extending the approach, namely: (i) become part of a toolbox of different valuation procedures; (ii) explicitly cope with uncertainty, and (iii) include different values for normalisation, in different regions worldwide. The software will be made available also in a stand alone version.

Goal and Background Geographical and technological differences in Life Cycle Inventory data are an important source for uncertainty in the result of Life Cycle Assessments. Knowledge on their impact on the result of an LCA is scarce, and also knowledge on how to manage them in an LCA case study.

Diese Arbeit hat das Ziel, eine praktisch anwendbare Methode zur Berechnung der Fehler in Ökobilanzen zu entwickeln. Die Entwicklung der Methode geschieht in der Arbeit mit Hilfe eines Modells, das die verschiedenen, heute üblichen Rechenschritte der Ökobilanz abbildet. Das Modell erlaubt es, die wahren Werte für die Fehler, die tatsächlichen Fehler , an verschiedenen Stellen der Ökobilanz wie z.B. in Zwischenergebnissen und insbesondere auch im Endergebnis der Ökobilanz, zu berechnen. Innerhalb des Modells wurden sechs verschiedene Methoden der Fehlerrechnung implementiert und untersucht. Wesentliches Kriterium war dabei die Darstellungsgüte der Methoden. Die Darstellungsgüte ist ein Maß dafür, wie gut der von einer Methode berechnete Fehlerwert dem Wert des tatsächlichen Fehlers entspricht. Der tatsächliche Fehler konnte so für eine Validierung der berechneten Fehlerwerte genutzt werden. Die entwickelte Methode unterscheidet die Berechnung zufälliger und systematischer Fehler. Zunächst werden systematische Fehler, also Fehler, die reproduzierbar sind, in den Daten korrigiert und falls erforderlich die Höhe der systematischen Fehler im Endergebnis berechnet. Diese Berechnungen und Korrekturen können vollkommen exakt durchgeführt werden. Ein Rückgriff auf Approximationsrechnungen ist nicht erforderlich. Auf Basis der um systematische Fehler korrigierten Werte werden anschließend die zufälligen Fehler berechnet. Als wesentlicher Parameter für die Darstellungsgüte hat sich der relative Fehler in Inputwerten der Rechnung erwiesen. Je nach Höhe dieses relativen Fehlers und je nach Rechenschritt der Ökobilanz empfiehlt das entwickelte Konzept, bestimmte Fehlerrechnungsmethoden nicht zu verwenden. Bei höheren Werten für den relativen Fehler ist insbesondere die Gaußsche Methode der Fehlerfortpflanzungsrechnung nicht mehr an-wendbar. In den Modellrechnungen hat sich gezeigt, daß sie dann den Fehler deutlich zu niedrig berechnet. Eine ebenfalls untersuchte Formel nach Bader/Baccini erbringt auch für höhere relative Fehler im Input Ergebniswerte, die nah an den tatsächlichen Werten liegen. Bei weiter erhöhten Werten für den relativen Fehler ist von den untersuchten Methoden nur noch die Monte Carlo Simulation in der Lage, den Fehler im Ergebnis gut abzubilden. Diese Methode stellt jedoch sehr hohe Anforderungen an die Zeit- und Hardwareressourcen, was den Einsatz in der praktischen Anwendung erschwert. Durch systematisch durchgeführte Parameteränderungen in den Modellrechnungen konnten für die Rechenschritte der Ökobilanz jeweils konkrete Werte für den relativen Fehler ermittelt werden, die wie Grenzwerte den Einsatz der Fehlerrechnungsmethoden beschränken. Der relative Fehler läßt sich in der praktischen Anwendung aus Inputwerten der Rechnung bzw. aus dem Ergebnis des vorangegangenen Rechenschritts bestimmen. Dieser Wert für den relativen Fehler ist dann mit dem ermittelten Grenzwerten abzugleichen, und aus diesem Abgleich läßt sich schließlich entscheiden, ob die Verwendung der Fehlerrechnungsmethode für den einzelnen Rechenschritt zulässig ist. The aim of this work is to develop a method for calculating errors in Life Cycle Assessments (LCAs), that can be applied in practice. This method is developed by putting up a model that covers the different calculation steps of a Life Cycle Assessment, as they are commonly used today. The model allows the calculation of true values for errors at different stages within the LCA calculation, and also in the final result of the LCA. In the model, six different methods for calculating errors where implemented and analysed. Essential for the analysis was the goodness of fit of each method. The goodness of fit is the measure to what extent the error, as calculated by an approximation method, corresponds to the true value of the error ( true error ). Thus, the true error was used to validate the calculated error. The error calculation method developed in this work distinguishes between the calculation of systematic errors and of random errors. At first, systematic errors, being errors that are reproducible, are cleared from the input data of the LCA, and if necessary or desired, the systematic errors in the result of the LCA can be calculated. Both clearing and calculation can be done in an exact way, without the need to refer to approximation formulas. In a second step, the random errors are calculated. The relative error turned out to be the essential parameter for discovering the goodness of fit of each method. Depending on the value of the relative error and the calculation step in the LCA, the method recommends not to use certain approximation formulas. The Gaussian error propagation formula turned out to largely underestimate the error, if the relative error had higher values. A formula developed by Bader and Baccini performed better in these cases. With even higher relative errors, from the methods analysed, only the Monte Carlo simulation was able to calculate the errors correctly. A systematic change of the parameter values accessible in each calculation step revealed distinct limits for the relative error specific for each calculation step in the LCA, and for each approximation formula. These limits span intervals specific for each approximation formula and each calculation step. In the calculation of an LCA, the relative error can be obtained either from input data or from a preceding calculation step. A check whether the error lies in the appropriate interval indicates whether an approximation formula should be used for calculating the errors in that calculation step.

Modelling data uncertainty is not common practice in life cycle inventories (LCI), although different techniques are available for estimating and expressing uncertainties, and for propagating the uncertainties to the final model results. To clarify and stimulate the use of data uncertainty assessments in common LCI practice, the SETAC working group ‘Data Availability and Quality’ presents a framework for data uncertainty assessment in LCI. Data uncertainty is divided in two categories: (1) lack of data, further specified as complete lack of data (data gaps) and a lack of representative data, and (2) data inaccuracy. Filling data gaps can be done by input-output modelling, using information for similar products or the main ingredients of a product, and applying the law of mass conservation. Lack of temporal, geographical and further technological correlation between the data used and needed may be accounted for by applying uncertainty factors to the non-representative data. Stochastic modelling, which can be performed by Monte Carlo simulation, is a promising technique to deal with data inaccuracy in LCIs.

This paper is a two-fold introduction. For one, it introduces a new, open source, LCA software. Second, it is to establish a new section in Int J LCA named ‘LCA Software’. Herewith, the editors of the journal recognise the growing possibilities and the impact of software, meaning both databases and calculation as well as modelling software, for practical applications as well as for the scientific development in LCA. This section is designed to house a broad variety of papers to beLCA focused and related to ICT (Information and Communication Technology). In this sense, announcements (as this one), conference reports, but also peer-reviewed papers on methodology and case studies, are most welcome.

Conservation and management of freshwater resources has become a major challenge of this century. In mining, one should be able to control the quantity of water intake, as well as the volume and quality of effluents. When the water loops in mining industry are closed, new methods for water quality control and optimisation at each process step are required, which facilitates also the recovery of additional valuable elements. The tightly closed water cycles necessitate that the tailings are filtered and stacked dry. Geopolymerisation of remaining tailings has the potential to be used for water and oxygen tight covers on the deposited tailings and for mine back-fill use. Detection of ore types and their classification or sorting by their geo-metallurgical properties before and in mill feed have a significant effect on metals recovery and on the process water quality in flotation. The main objective of this paper is to review the progress made towards a new paradigm shift at mine sites to recycle water and valorise tailings for an improved environmental and economic result, and for increased social acceptance of mine operations.