Education in 'life cycle sustainability assessment': caring for all 3 P's in one

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

Integrating life cycle assessment tools and information with product life cycle management : Product data management

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

Social assessment of raw materials supply chains: A life-cycle-based analysis

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 Resource

Error calculation in Life Cycle Assessments

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

Cost data quality considerations for eco-efficiency measures

Cost data are a central aspect of eco-efficiency measures, either as means to assess value of production, or, more directly, as one dimension of the efficiency ratio. Several aspects may affect the quality of cost data, among them definitions, time and space, and confidentiality issues. Somewhat surprisingly, cost data quality has received little attention in the field of sustainability and eco-efficiency so far. Even worse, perhaps, is the lack of tools suitable for a cost data quality assessment and management. This paper discusses parameters that affect cost data quality, and will then propose a pedigree matrix as a tool designed for managing cost data quality issues. The application of the matrix is described, also in combination with a previously proposed, and broadly used, pedigree matrix for environmental data quality management.Life cycle costs Cost data quality Pedigree matrix NUSAP scheme Eco-efficiency

Life Cycle Inventory Analysis: Methods and Data

Provides an analysis of the second phase in the LCA Framework.\ua0Discusses the history of inventory analysis from the 1970s through SETAC and the ISO standard.\ua0Highlights the Link Between Life Cycle Inventory Analysis and Life Cycle Impact Assessment