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Life Cycle Management

Life Cycle Management

Being an integrated management framework of concepts, techniques and procedures, LCM connects different operational concepts, policies, systems, methods, tools and data that incorporate environmental, economic and social aspects and looks how they are interconnected and how to best address these throughout the product or process life cycle. As indicated in the previous section, a wide range of methods, tools and concepts can be used in LCM. Analytical tools are life cycle assessment (LCA), life cycle costing (LCC), social life cycle assessment (SLCA), organizational LCA (OLCA), hotspot analysis, different forms of footprinting such as water footprint and carbon footprint, cost benefi t analysis (CBA), material fl ow analysis (MFA), substance fl ow analysis (SFA), input–output analysis (IOA), environmental risk assessment (ERA), etc. Procedural tools include auditing, checklists, eco-design, eco-labeling, etc. and supportive tools such as weighting, e.g. by Delphi expert panels, uncertainty analy- sis, sensitivity analysis, etc. could be applied. LCM also includes design concepts such as design for the environment, design for sustainability, design for recycling etc. It also refers to policies and strategies such as circular economy, sustainable consumption and production, integrated product policy (IPP), resource effi ciency, eco-effi ciency, dema- terialization, industrial ecology, etc. as well as organizational systems or programs such as extended product responsibility (EPR), product development process (PDP), certifi cation, environmental communication, value chain management, etc. All these analytical and procedural tools as well as policies, strategies and systems/programs are
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Time preferences over the life cycle

Time preferences over the life cycle

discount rate by 0.48 percentage points. Evaluated at the average discount rate in the sample of 14.14, this amounts to a 3.4% decrease in the discount rate. Both the CPI and real interest rates are positively related to the measured discount rates. Our finding of diminishing discount rates over the life cycle is similar to empirical findings in experimental studies ( Green et al. , 1994 ; Tanaka et al. , 2010 ) and field stud- ies ( Warner and Pleeter , 2001 ; Bishai , 2004 ). While investigating the mechanism behind the age profile is beyond the scope of the present paper, there are some existing theo- ries consistent with our result. Using an evolutionary biology approach, Rogers ( 1994 ) shows that age-dependent reproductive potential generates a decreasing age profile for subjective discount rates among sexually matured adults. Halevy ( 2005 ) also shows that diminishing impatience would emerge for a decision maker with time-consistent prefer- ences when lifetime is uncertain. 14
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Life cycle sustainability assessment approaches for manufacturing

Life cycle sustainability assessment approaches for manufacturing

Sustainability and sustainable manufacturing are relevant topics for governments and industries worldwide. In that pursuit, various concepts for sustainability exist and approaches for sustainability assessment have already been introduced. Nevertheless evaluating the sustainability performance at the product level remains a challenge. One of the most widespread concepts of sustainability lies in the triple-bottom-line theory, which considers environmental, economic and social aspects (Finkbeiner et al. 2010 ; Remmen et al. 2007 ; Elkington 1998 ). Moreover, with regard to assessing the sustainability performance of products and processes, life cycle thinking approaches which include the whole life cycle from “cradle to grave, ” are increasingly gaining in importance. By employing such approaches, a shifting of impact between the different life stages and sustainability dimensions can be identi fied and avoided (Finkbeiner et al. 2010 ).
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Life Cycle Based Comparison of Textile Ecolabels

Life Cycle Based Comparison of Textile Ecolabels

4.4. Comparison of the Label Requirements and Environmental Hotspots Identified by PEF The identified label requirements are compared with the environmental hotspots identified by PEF. The comparison was performed based on the PEF study for the impacts water use and air emissions (see Section 3 ). For other impacts addressed by the labels and analysed in this work (toxicity, land use, and recycling), no hotspot data was available. The hotspot in the impact water use was identified based on the impact categories water scarcity (i.e., water consumption) as well as acidification and freshwater eutrophication (i.e., water pollution). Only two labels—Blue Angel Textiles and CmiA—provide specific requirements for water use in the raw material production stage, whereas only CmiA considers water consumption, e.g., by prohibiting cotton production under irrigation and setting goals for the application of water conservation techniques. A clear environmental hotspot with regard to water pollution occurs in the textile manufacturing phase. Here, all analysed labels (except CmiA that considers only raw material production phase) provide requirements with regard to the quality of discharged water, e.g., by setting thresholds for specific pollutants or requiring compliance with local legislation. In contrast to material production and textile manufacturing phase, water use aspects in the garment use phase are not addressed by any of the analysed labels, although this stage contributes to one-third of the total water scarcity impact in the life cycle of textiles (see Table 3 ). The hotspot for the impact on air emissions was identified based on the impact category climate change considered in the PEFCR. Still, it should be noted that air emissions addressed by the labels include not only the pollutants that contribute to global warming, but a broader set of substances. The first hotspot arises in the life cycle stage raw material production, which contributes to over 20% of the total impact (see Table 3 ). Out of seven analysed labels, only the Blue Angel Textiles sets specific requirements on air emissions for the raw material production. The latter include thresholds for sulphur compound emissions, volatile organic compounds (VOCs), and nitrogen oxides. Air emissions in the textile manufacturing phase contribute to over one-third of the total impact. This hotspot is addressed by all analysed labels (except CmiA) using specific requirements. In contrast, air emissions in the use stage, which according to PEFCR has around 8% of the total impact, are addressed by only one label: GOTS.
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Income risk over the life cycle and the business cycle

Income risk over the life cycle and the business cycle

In recent research, I try to understand the nature of the uncertainty that major labor market events generate for workers. There are three main dimensions of this research, which studies how individuals’ income uncertainty and risk varies over the business cycle and over the life cycle, and how it has changed over the last four decades. The answers to these questions are of immediate relevance for both deepening our knowledge of labor market dynamics and for informing social insurance debates, such as those surrounding Social Security reform, unemployment insurance policy, the degree of job protection, and the pro- gressivity of the tax system. Each of these policies seeks to moderate various types of individual risk.
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Health Dynamics Shape Life-Cycle Incomes

Health Dynamics Shape Life-Cycle Incomes

Notably, health unfolds qualitatively and quantitatively different effects on income per person across age cohorts. Life expectancy positively affects average wages and incomes of prime-age workers aged 25–54 years, whereas it exerts no or even a negative effect on average incomes for workers under age 24 and over age 55. The health effects are largest for the age cohort 25–34 and decrease from this age onward. Overall, the estimates are slightly more pronounced for total income per person in Panel (b). High values of the F-statistics document a strong first-stage correlation between life expectancy and the instrument throughout all specifications. The estimates are statistically significant at conventional levels for workers aged 15–44 and the overall workforce in Panel (a) and for all age cohorts except the cohort aged 55–64 in Panel (b). Moreover, the positive estimates for prime-age workers significantly differ from the negative estimates for the workers under age 24 and over age 55. Hence, the results reveal meaningful heterogeneity with respect to the health effects across age cohorts. Following the cardiovascular revolution, life-cycle income profiles slope more strongly at the beginning and the end of the standard work life. Compared to previous generations, age thus becomes a more prominent determinant of income dynamics. Nevertheless, the age at which life-cycle productivity peaks remains roughly stable. In 1960, this age lies between 40 and 50. While the large health estimates for young working ages suggest a shift of productivity peaks toward younger ages, the health improvements are larger for older working ages, such that both effects balance each other. Hence, the health improvements of the cardiovascular revolution do not compensate potential adverse effects of an aging workforce by shifting the age of productivity peaks.
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Life cycle sustainability assessment at an automotive company

Life cycle sustainability assessment at an automotive company

the target of the method was to identify hotspots to prioritize primary data collection and enable quantitative evaluation of the social dimension within LCSA, these results did not bring about any benefit as no differentiation between materials was possible. That changed after removing the KO criterion in the last aggregation step. Which means, however, that the risk of human rights violations in the production countries of a material can be compensated for by less critical risks for other social topics. This mitigation of the evaluation logic was nevertheless accepted as it gives practitioners the possibility to focus on a few materials with high risk and actually be able to initiate next steps to improve transparency and remediating these risks. When no differentiation between materials is possible, the challenge of addressing all at once might prove to be a too sumptuous task or a focusing on a few material out of these might be perceived as arbitrary and thus lose credibility as a measure within a company. The presented approaches to identify social hotspots still only constitute the first step of an S-LCA. The social risk or social hotspot analysis functions as a guide as to where to focus the data collection efforts. That means, primary data has still to be gathered for the critical supply chains, in order to be able to carry out a substantiated S-LCA. The wish of parts of the S-LCA community to also include the assessment of positive impacts (Sala et al., 2015; Petti et al., 2018) has not been addressed by this research. The missing of a complete method definition of S-LCA, especially regarding the impact assessment (Arcese et al., 2018; Petti et al., 2018) was not seen as much as a challenge as the missing of a clear indicator set (Sala et al., 2015). In addition to the challenge of data acquisition and evaluating social impacts for S-LCA, it is still not clearly defined how social impacts in the use phase should be addressed and related to the functional unit, let alone for single components of a product. The integration of different affected stakeholders in different life cycle stages with respective social impacts remains a challenge.
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Time preferences over the life cycle

Time preferences over the life cycle

1 Introduction A key assumption of the discounted-utility model ( Samuelson , 1937 ) and its variants in- cluding the life-cycle model is that time preferences are stable over the life cycle. Since these models are a workhorse for modern economic analyses, the validity of this assump- tion has important implications for many of welfare analyses and policy evaluations. This assumption is also a foundation for structural estimation of time preferences us- ing consumption Euler equations. 1 However, it has been challenging to test whether and how time preferences change with age because there is a well-known identification prob- lem; without data with a long time horizon, disentangling age effects from influences of period-specific and cohort-specific factors is impossible. This identification problem might explain why previous studies, all relying on short panel or cross-sectional data, find mixed results about the age pattern of time preferences. 2
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Inequality in Personality over the Life Cycle

Inequality in Personality over the Life Cycle

2011 ) and lower levels of education ( Almlund et al. , 2011 ; Lundberg , 2013 , 2018 ). We model inequalities in the age profiles of personality traits and facets by gender and socioeconomic status (SES). Establishing how these multi-faceted skills differ by age, gender, and socioeconomic status is essential for discussions of theory and empirical evidence related to, e.g., models of household production, optimal taxation, and the roots of gender pay gaps. To the best of our knowledge, we are the first to document age-related personality differences by both education and income. We collected a comprehensive survey in 2019 on a large Danish population (N=38,711), which included, among others, the Big Five Inventory-2 (BFI-2). The BFI-2 uses 60 items (30 in the abbreviated version) to hierarchically assess the Big Five personality domains, together with 15 more specific facet traits ( Soto and John , 2017a , b ). We linked the survey responses at the individual level to high-precision administrative register data, which allow us to classify individuals according to their own and parental SES. Thanks to this large sample, we can precisely estimate life-cycle profiles of personality by gender and SES. We model potential non-linearities in the age-personality relationships flexibly with bivariate kernel regression methods without imposing any specific functional form ( Wand and Jones ,
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Teaching life cycle assessment in higher education

Teaching life cycle assessment in higher education

The teaching starts in general by introducing the wider context of LCA (e.g. sustainable development, resource efficiency, circular economy, environmental management and eco‑design), the basic understanding of environmental impacts (e.g. climate change and acidification) and the con‑ cept of life cycle thinking as a way of providing a holistic understanding of value chains and product systems. Often jointly with this concept, the trade‑offs or burden shifting across life cycles and impacts is explained. This broad intro‑ duction is followed by the description of the purposes of LCA jointly with illustrations of the lectures by use cases corresponding to the students’ area of study. As a next step, generally the ISO 14,040/44 framework including the LCA phases and its terminology (functional unit, system boundaries etc.) are clarified. These teaching contents are considered necessary to achieve the learning outcomes that students understand life cycle thinking and its utilization and relevance.
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Workers, firms and life-cycle wage dynamics

Workers, firms and life-cycle wage dynamics

Heterogeneity at labor market entry is substantial, see Panel A of Table 1. The dispersion of life-cycle shocks diminishes between 26 and 45 years of age, reflecting the concave evolution of wage differentials before age 45 shown in Figure 2.B. Both diminishing heterogeneous returns to general human capital or a reduction in the rate of workers mobility and of the associated heterogeneous wage changes could explain this pattern. The evidence is different after age 50, when we estimate a substantial increase in the dispersion of shocks that is again in line with the evidence of Figure 2.B. Heterogenous labor supply behaviour may explain this finding, as some workers may start reducing daily hours approaching retirement. In the limit, some may also start leaving the labor force, and the observed pattern may be the outcome of selective participation, if surviving workers are polarised at the tails of the wage distribution. Having model parameters that are specific to this age range ensures that any bias induced by selective participation is isolated and not transmitted to the rest of the model.
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Knowledge recombination along the technology life cycle

Knowledge recombination along the technology life cycle

May 26, 2016 Abstract Technological evolution is a dynamic process which is intimately related to the technol- ogy’s knowledge base. A knowledge base shows certain dynamics and evolves over time. This study aims to understand how recombination of different kinds of knowledge influences the knowledge base along the technology life cycle. For this purpose, the Anderson and Tushman ( 1990 ) technology life cycle model is extended to account for the technology’s knowledge base and proposes different kinds of knowledge which are relevant in the life cycle phases. This model is empirically tested for wind power and photovoltaics in Germany from 1970 until 2006. Patent’s forward citations are considered as recombinatorial success and inventors’ patenting experience proxies different kinds of knowledge. Negative binomial regressions as well as rolling-window regressions are used to capture the relevance of different kinds of knowledge along the technology life cycle. Results reveal that different kinds of knowledge matter along the technology life cycle. In the era of ferment, knowledge from domains ex- ternal to the technology is relevant, but for the dominant design and the era of incremental change new and specialized knowledge is most important. However, there are technological differences. Here, rolling-window regressions prove to be a useful analytical tool to reveal nuanced changes over time. The results have several policy and management implications, especially whom to fund or hire for inventive activity.
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Researchers' Career Transitions over the Life Cycle

Researchers' Career Transitions over the Life Cycle

Heterogeneous Changes in Time Use The aging of researchers decreases the time spent on research, on average. Is the decline because hard workers slow down or because slackers cease conducting research? Aging increases the time use for administrative tasks. Is the increase concentrated on a small number of researchers who choose a career of being an administrator, or does almost every senior researcher more or less spend their time on administrative tasks? These questions are about the heterogeneous changes of time use over the life cycle that cannot be answered by simply looking at the evolutions of average time use. To address these questions, we estimate the quantile regression models that estimate the 25 th , 50 th , and 75 th percentiles of time-use distributions conditional on age and other demographic variables that were included in the previous regression models. Table 4a reports the estimates without controls for academic ranks, and Table 4b reports those with controls for academic ranks.
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Markov-chain approximations for life-cycle models

Markov-chain approximations for life-cycle models

Suggested Citation: Fella, Giulio; Gallipoli, Giovanni; Pan, Jutong (2017) : Markov-chain approximations for life-cycle models, Working Paper, No. 827, Queen Mary University of London, School of Economics and Finance, London This Version is available at: http://hdl.handle.net/10419/184778

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Workers, Firms and Life-Cycle Wage Dynamics

Workers, Firms and Life-Cycle Wage Dynamics

permanent components of this AKM-type model are reported in Column 2 of Table 3. Perhaps the best way to compare these estimates with the ones from the baseline model is to look at the implied average inequality decomposition for the overall period 1985 - 2016. To ease comparisons, Table 4 reports these decompositions for the baseline model and for the AKM version of the model without life-cycle variation. The overall variance imputable to individual heterogeneity (life-cycle and match components) is larger in the AKM model (50 vs 40 percent), which also attributes more than half of this variance to the match effect, while the baseline model weights more the life-cycle component. What is striking is the different balance between firm and sorting effects: while the baseline gives more weight to the latter, the AKM specification attributes more weight to the ‘pure’ firm effect. It is worth noting that the sorting effect has a life-cycle aspect in the baseline and that empirically wage inequality has a strong life-cycle variation, that cannot be detected by the life-cycle constant AKM specification. The estimated share of inequality imputable to the sorting component more than halves compared with the baseline and is now in lines with figures reported by Card, Heining and Kline (2013, about 10 percent on average)
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The Persistence of the Standardized Life Cycle

The Persistence of the Standardized Life Cycle

It is obvious that the ideal life cycle will be affected by post-secondary educa- tion and must in some way differ between, on the one hand the people who stop studying before or after the completion of secondary schooling and, on the other, those who pursue tertiary education. The way in which this adaptation takes place can shed light on the extent to which the life cycle is destandardized or individualized. Indeed, people pursuing tertiary education are likely to feel the cross-pressures generated by the existing ideal life cycle on the one hand, and the expectation to successfully pursue their studies on the other. It is conceivable that they adapt by destandardizing the life cycle, frequently deviating from the ideal sequence or even inversing the sequence, and by showing greater variation in the timing of transition than the rest of the population. In that sense they could act as a kind of avant-garde of the destandardization of the life cycle. Some authors do indeed expect them to fulfil that role (Kuijsten, 1999; Dykstra, 2003). It is, however, also conceivable that they respect the ideal sequence, do not show more variation in timing than the rest of the population, and adapt to their situation by simply postponing those transitions that are subsequent to the com- pletion of their studies. The latter would be a minimal or homeostatic adaptation, leaving the existing pattern (the ideal life cycle) as unchanged as possible. If that turns out to be the case, it would be a very strong argument in favour of the persistence of the standardized life cycle.
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Life Cycle Assessment für ein containerbasiertes Wohngebäude unter Berücksichtigung des Autarkiegrades

Life Cycle Assessment für ein containerbasiertes Wohngebäude unter Berücksichtigung des Autarkiegrades

Sobald allerdings das R ecyclingpotenzial aus dem Modul D mit in die Bilanzierungsgrenzen einbezogen wird, kommen eine große Menge an Gutschriften hinzu, die die Ö kobilanz weiter verbessern. Diese werden bei hohen Autarkiegraden vor allem von dem ins Netz eingespeisten Strom erzeugt. Das sorgt dafür, dass die Summe der Umweltbelastung aus Herstellung, Nutzung, Verwertung und R ecyclingpotenzial bei hohen Autarkiegraden deutlich reduziert wird. Da es sich bei Modul D aber um ein Potenzial handelt, welches zwar möglicherweise nicht aber zwangsläufig eintreten muss, kann eine Bewertung des Life Cycle Assessment ohne dieses Modul zur Beurteilung der entstandenen Umweltbelastungen sinnvoll sein.
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Enhanced product functionality with life cycle units

Enhanced product functionality with life cycle units

The LCU system consists of four elements: sensor, marking, life cycle board (LCB) and actuator, as can be seen in Fig. 2. Sensors, or transducers, are integrated in components, acquire the product status and monitor safety-relevant joining elements. Information may be displayed in a coded way with the help of markings in order to ease their sensorial acquisition and decoding. The LCB is able to process, store and transfer information with its three elements: processor, memory and interface. Actuators may act against the e€ ects of physical changes and are controlled by the LCB. A second task of actuators is the intelligent disassembly by using joining elements with integrated actuators. To disassemble the joint, an electromagnet has to be positioned close to the head. After activating the electromagnet, the joint may be released.
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From life-cycle assessment towards life-cycle design of carbon dioxide capture and utilization

From life-cycle assessment towards life-cycle design of carbon dioxide capture and utilization

LCA databases provide data for single processes, partial life cycle stages or complete life cycles. For an overview of LCA databases, the reader is referred to Table 5.1 in Curran (2012). Particularly relevant databases for production of chemicals are ecoin- vent v3 (ecoinvent, 2010) and Eco-profiles (PlasticsEurope, 2014). The ecoinvent v3 database contains more than 9,000 datasets that cover all sorts of products from elec- tricity to chemicals, food, transport, recycling and disposal (ecoinvent, 2010). A major advantage of the datasets in ecoinvent is the transparency as usually data for each process can be accessed in detail. The freely available Eco-profiles by PlasticsEurope offer aggregate cradle-to-gate life cycle inventories for plastics, basic precursors and on-site energy such as electricity and steam (PlasticsEurope, 2014). When using data from LCA databases or other sources, the suitability of data for the present LCA study should be checked: for example, data can be specific for one technology or averaged across production technologies.
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Integrating life cycle assessment tools and information with product life cycle management

Integrating life cycle assessment tools and information with product life cycle management

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.
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