Rigorous proof of fuzzy error propagation with matrix-based LCI

Background, aim, and scope  

Propagation of parametric uncertainty in life cycle inventory (LCI) models is usually performed based on probabilistic Monte Carlo techniques. However, alternative approaches using interval or fuzzy numbers have been proposed based on the argument that these provide a better reflection of epistemological uncertainties inherent in some process data. Recent progress has been made to integrate fuzzy arithmetic into matrix-based LCI using decomposition into α-cut intervals. However, the proposed technique implicitly assumes that the lower bounds of the technology matrix elements give the highest inventory results, and vice versa, without providing rigorous proof.     

Life cycle assessment of automotive fuels: critical analysis and recommendations on the emissions inventory in the tank to wheels stage

Purpose  

As new alternative automotive fuels are being developed, life cycle assessment (LCA) is being used to assess the sustainability of these new options. A fuel LCA is commonly referred as a “Well To Wheels” analysis and calculates the environmental impacts of producing the fuel (the “Well To Tank” stage) and using it to move a car (the “Tank To Wheels” stage, TTW). The TTW environmental impacts are the main topic of this article.     

Parameter uncertainty in LCA: stochastic sampling under correlation

Purpose  

At the parameter level, data inaccuracy, data gaps, and the use of unrepresentative data have been recognized as sources of uncertainty in life cycle assessment (LCA). In many LCA uncertainty studies, parameter distributions were created based on the measured variability or on “rules of thumb,” but the possible existence of correlation was not explored. The correlation between parameters may alter the sampling space and, thus, yield unrepresentative results. The objective of this article is to describe the effect of correlation between input parameters (and the final product) on the outcome of an uncertainty analysis, carried out for an LCA of an agricultural product.     

Using fuzzy numbers to propagate uncertainty in matrix-based LCI

Background, aim, and scope  Analysis of uncertainties plays a vital role in the interpretation of life cycle assessment findings. Some of these uncertainties arise from parametric data variability in life cycle inventory analysis. For instance, the efficiencies of manufacturing processes may vary among different industrial sites or geographic regions; or, in the case of new and unproven technologies, it is possible that prospective performance levels can only be estimated. Although such data variability is usually treated using a probabilistic framework, some recent work on the use of fuzzy sets or possibility theory has appeared in the literature. The latter school of thought is based on the notion that not all data variability can be properly described in terms of frequency of occurrence. In many cases, it is necessary to model the uncertainty associated with the subjective degree of plausibility of parameter values. Fuzzy set theory is appropriate for such uncertainties. However, the computations required for handling fuzzy quantities has not been fully integrated with the formal matrix-based life cycle inventory analysis (LCI) described by Heijungs and Suh (2002). Materials and methods  This paper integrates computations with fuzzy numbers into the matrix-based LCI computational model described in the literature. The approach uses fuzzy numbers to propagate the data variability in LCI calculations, and results in fuzzy distributions of the inventory results. The approach is developed based on similarities with the fuzzy economic input–output (EIO) model proposed by Buckley (Eur J Oper Res 39:54–60, 1989). Results  The matrix-based fuzzy LCI model is illustrated using three simple case studies. The first case shows how fuzzy inventory results arise in simple systems with variability in industrial efficiency and emissions data. The second case study illustrates how the model applies for life cycle systems with co-products, and thus requires the inclusion of displaced processes. The third case study demonstrates the use of the method in the context of comparing different carbon sequestration technologies. Discussion  These simple case studies illustrate the important features of the model, including possible computational issues that can arise with larger and more complex life cycle systems. Conclusions  A fuzzy matrix-based LCI model has been proposed. The model extends the conventional matrix-based LCI model to allow for computations with parametric data variability represented as fuzzy numbers. This approach is an alternative or complementary approach to interval analysis, probabilistic or Monte Carlo techniques. Recommendations and perspectives  Potential further work in this area includes extension of the fuzzy model to EIO-LCA models and to life cycle impact assessment (LCIA); development of hybrid fuzzy-probabilistic approaches; and integration with life cycle-based optimization or decision analysis. Additional theoretical work is needed for modeling correlations of the variability of parameters using interacting or correlated fuzzy numbers, which remains an unresolved computational issue. Furthermore, integration of the fuzzy model into LCA software can also be investigated.     

Mutual Gains and Distributive Ideologies in South Africa: Theorizing Negotiations between Communities and Protected Areas

With the rise of joint management of protected areas, community representatives are increasingly involved in formal negotiations with state officials, nongovernmental organizations (NGOs) and other actors. Policy recommendations have commonly idealized “win-win” scenarios. Theoretical work on negotiation from psychology and management studies, however, points to identifiable circumstances under which the goal of a mutually beneficial “win-win” situation may limit the strategies, and ultimately the benefits, available to communities. Instead, an antagonistic, “distributive” approach to negotiations may be more compatible with the pressures on and strategies available to community representatives. The tensions between a “mutual gains” and “distributive” approach to negotiations are evident in two land claims on protected areas in South Africa: the Dwesa-Cwebe Nature Reserves, and the Pafuri Triangle, a portion of Kruger National Park. In each, NGOs that operated with a “mutual gains” strategy, espousing a “win-win” scenario, came to be perceived as collaborating with conservation agencies. Meanwhile, as negotiation theory would suggest, community representatives inclined towards a “distributive” strategy and allied with a second set of explicitly advocatory NGOs. Expecting that communities should embrace a “win-win” scenario from the outset is unrealistic and likely to reduce communities’ power in negotiations.

Bert L. Vallee, “Mr. Zinc” (1919–2010)

Obituary

Bert L. Vallee, “Mr. Zinc” (1919–2010)     

A comparison of cut roses from Ecuador and the Netherlands

Purpose  

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.     

Inventory, differentiation, and proportional diversity: a consistent terminology for quantifying species diversity

Almost half a century after Whittaker (Ecol Monogr 30:279–338, 1960) proposed his influential diversity concept, it is time for a critical reappraisal. Although the terms alpha, beta and gamma diversity introduced by Whittaker have become general textbook knowledge, the concept suffers from several drawbacks. First, alpha and gamma diversity share the same characteristics and are differentiated only by the scale at which they are applied. However, as scale is relative––depending on the organism(s) or ecosystems investigated––this is not a meaningful ecological criterion. Alpha and gamma diversity can instead be grouped together under the term “inventory diversity.” Out of the three levels proposed by Whittaker, beta diversity is the one which receives the most contradictory comments regarding its usefulness (“key concept” vs. “abstruse concept”). Obviously beta diversity means different things to different people. Apart from the large variety of methods used to investigate it, the main reason for this may be different underlying data characteristics. A literature review reveals that the multitude of measures used to assess beta diversity can be sorted into two conceptually different groups. The first group directly takes species distinction into account and compares the similarity of sites (similarity indices, slope of the distance decay relationship, length of the ordination axis, and sum of squares of a species matrix). The second group relates species richness (or other summary diversity measures) of two (or more) different scales to each other (additive and multiplicative partitioning). Due to that important distinction, we suggest that beta diversity should be split into two levels, “differentiation diversity” (first group) and “proportional diversity” (second group). Thus, we propose to use the terms “inventory diversity” for within-sample diversity, “differentiation diversity” for compositional similarity between samples, and “proportional diversity” for the comparison of inventory diversity across spatial and temporal scales. Electronic supplementary material  The online version of this article (doi:) contains supplementary material, which is available to authorized users.     

Pregnancy outcome of women in the vicinity of nuclear power plants in Taiwan

The purpose of the study was to investigate whether proximity to nuclear power plants may increase the risk of abnormal pregnant outcomes among the resident women. In this ecological study, data were used from the Health Services Birth Reports Database established by the Bureau of Health Promotion, National Department of Health, Taiwan, in 2001–2004. Chi-square-tests were carried out to investigate the “Plant-vicinity” and “Non plant-vicinity” group in terms of pregnancy outcome. Additionally, logistic regression was performed to investigate whether residence in the vicinity of a nuclear power plant was related to any abnormal pregnancy results. Based on data from 5,679 included subjects, no difference was observed between pregnancy outcomes of the “Plant-vicinity” and “Non plant-vicinity” groups. After accounting for possible confounders, the adjusted odds ratios were 1.20 (95% CI = 0.56–2.56) for stillbirth, 1.21 (95% CI = 0.95–1.53) for premature birth, 1.04 (95% CI = 0.79–1.37) for low birth weight, and 1.58 (95% CI = 0.85–2.93) for congenital deficiencies, respectively, when comparing the “Plant-vicinity” with the “Non plant-vicinity” group. The results of the study indicate that residence in the vicinity of a nuclear power plant is not a significant factor which will cause abnormal health situations during pregnancy.     

A hybrid life cycle assessment model for comparison with conventional methodologies in Australia

Background, aim, and scope  

One barrier to the further implementation of LCA as a quantitative decision-support tool is the uncertainty created by the diversity of available analytical approaches. This paper compares conventional (‘process analysis’) and alternative (‘input–output analysis’) approaches to LCA, and presents a hybrid LCA model for Australia that overcomes the methodological limitations of process and input–output analysis and enables a comparison between the results achieved using each method. A case study from the water industry illustrates this comparison.     

Towards a custom chelator for mercury: evaluation of coordination environments by molecular modeling

Abstract  

A chelator is a molecule which binds a metal or metalloid ion by two or more functional groups to form a stable ring complex known as a chelate. Despite the widespread clinical use of so-called chelation therapy to remove mercury, none of the drugs currently in use have been shown to chelate mercury. Mercury can adopt three common coordination environments: linear diagonal, trigonal planar, and tetrahedral. We have previously discussed some of the structural criteria for optimal binding of mercury in linear-diagonal coordination with thiolate donors (George et al. in Chem. Res. Toxicol. 17:999–1006, 2004). Here we employed density functional theory and X-ray absorption spectroscopy to evaluate the ideal chain length for simple alkane dithiolate chelators of Hg²⁺. We have also extended our previous calculations of the optimum coordination geometries to the three-coordinate [Hg(SR)₃]⁻ case. Finally, we propose a new chelator “tripod” molecule, benzene-1,3,5-triamidopropanethiolate, or “Trithiopod,” which is expected to bind Hg²⁺ in three-coordinate geometry with very high affinity.     

Post-consumer waste wood in attributive product LCA

Background  

In product life cycle assessment (LCA), the attribution of environmental interventions to a product under study is an ambiguous task. This is due to a) the simplistic modeling characteristics in the life cycle inventory step (LCI) of LCA in view of the complexity of our techno-economic system, and b) to the nontangible theoretical nature of the product system as a representation of the processes ‘causally’ linked to a product. Ambiguous methodological decisions during the setup of an LCI include the modeling of end-of-life scenarios or the choice of an allocation factor for the allocation of joint co-production processes. An important criterion for methodological decisions — besides the conformity with the relevant series of standards ISO 14 040 — is if the improvement options, which can be deduced from the LCI, are perceived by the decision-maker as to redirect the material flows at stake into more sustainable paths.     

Introducing the UNEP/SETAC methodological sheets for subcategories of social LCA

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.     

The carbon footprint measurement toolkit for the EU Ecolabel

Background, aim and scope  

Established in 1992, the European Union Ecolabel, that is briefly called “the Flower” because of the mark, is a voluntary ecological product award issued by the 1980/2000 Regulation (EC 2000). Adopting the ISO classification, the EU Ecolabel belongs to the “Type I environmental labelling” (ISO 14024:1999). The possibility to include GreenHouse Gases (GHG) emissions (as of CO₂ equivalents) among the EU Ecolabel criteria is a news that is justified to the consideration that, in the last 30 years, their management and limitation assumed a relevant and strategic importance for greenhouse effect control. This paper introduces results of a project for the European Commission that aimed at developing and checking a carbon footprint calculator procedure suitable for the inclusion of the GHG emission issue in the EU Ecolabel criteria. The output tool is primarily aimed at the policy maker, i.e. the European Commission, the European Union Ecolabel Board and the Ad Hoc Working Group (AHWG, created to develop a transparent and wide discussion with reference stakeholders, see Fig. 2 for more details), but, in this step, not directly to the applicant yet.     

Hierarchical Categorical Perception in Sensing and Cognitive Processes

This article considers categorical perception (CP) as a crucial process involved in all sort of communication throughout the biological hierarchy, i.e. in all of biosemiosis. Until now, there has been consideration of CP exclusively within the functional cycle of perception–cognition–action and it has not been considered the possibility to extend this kind of phenomena to the mere physiological level. To generalise the notion of CP in this sense, I have proposed to distinguish between categorical perception (CP) and categorical sensing (CS) in order to extend the CP framework to all communication processes in living systems, including intracellular, intercellular, metabolic, physiological, cognitive and ecological levels. The main idea is to provide an account that considers the heterarchical embeddedness of many instances of CP and CS. This will take me to relate the hierarchical nature of categorical sensing and perception with the equally hierarchical issues of the “binding problem”, “triadic causality”, the “emergent interpretant” and the increasing semiotic freedom observed in biological and cognitive systems.

The anthropogenic stock extended abiotic depletion potential (AADP) as a new parameterisation to model the depletion of abiotic resources

Purpose  

Raw material availability is a cause of concern for many industrial sectors. When addressing resource consumption in life cycle assessment (LCA), current characterisation models for depletion of abiotic resources provide characterisation factors based on (surplus) energy, exergy, or extraction–reserve ratios. However, all indicators presently available share a shortcoming as they neglect the fact that large amounts of raw materials can be stored in material cycles within the technosphere. These “anthropogenic stocks” represent a significant source and can change the material availability significantly. With new characterisation factors, resource consumption in LCA will be assessed by taking into account anthropogenic material stocks in addition to the lithospheric stocks. With these characterisation factors, the scarcity of resources should be reflected more realistically.     

Relative amounts of sialic acid and fucose of amniotic fluid glycoconjugates in relation to pregnancy age

The present knowledge concerning the glycan structures and role of glycoconjugates derived from amniotic fluid is fragmentary and mainly focuses on the individual glycoproteins. The question has arisen as whether the general glycosylation pattern of amniotic fluid glycoconjugates can change with the progression of a normal pregnancy. In the present work we have described the dynamic, quantitative alterations in relative amounts of sialic acid and fucose linked by a variety of anomeric linkages to subterminal oligosaccharide structures of amniotic fluid glycoconjugates in relation to pregnancy age. The analysis was performed in the following groups of amniotic fluids derived from normal pregnancy by lectin dotting method: “2nd trimester” (14–19 weeks), “3rd trimester” (29–37 weeks), “perinatal period” (38–40 weeks) , “delivery at term” (39–41 weeks) and “post date pregnancy” (41–43 weeks). In the “3rd trimester” the amniotic fluid glycoconjugates contained higher relative amounts of glycans terminated by α2-6-linked sialic acid (p < 0.00002) and by α1-6 innermost fucose (p < 0.000001) than those in the 2nd trimester. In contrast, they showed the lower relative amount of fucose linked α1-3 (p < 0.02). At the perinatal period the relative amount of α2-6-linked sialic acid increased (p < 0.03), and it then decreased during delivery (p < 0.02) to the level found in the “3rd trimester” group. In the post date pregnancy all parameters studied increased. The sialyl- and fucosyl-glycotopes of the amniotic fluid glycoconjugates may play an critical role in growth and tissue remodeling of the foetus, as well as may might reflect maturation of a foetus. Additionally, a determination of the glycotope expressions might be helpful in prenatal diagnosis as predictor factors for well being of mother and child.     

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

Purpose

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

Methods

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

Results and discussion

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

Conclusions

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