A Novel Approach to Characterize Data Uncertainty in Material Flow Analysis and its Application to Plastics Flows in Austria

Material flow analysis (MFA) is widely used to investigate flows and stocks of resources or pollutants in a defined system. Data availability to quantify material flows on a national or global level is often limited owing to data scarcity or lacking data. MFA input data are therefore considered inherently uncertain. In this work, an approach to characterize the uncertainty of MFA input data is presented and applied to a case study on plastics flows in major Austrian consumption sectors in the year 2010. The developed approach consists of data quality assessment as a basis for estimating the uncertainty of input data. Four different implementations of the approach with respect to the translation of indicator scores to uncertainty ranges (linear‐ vs. exponential‐type functions) and underlying probability distributions (normal vs. log‐normal) are examined. The case study results indicate that the way of deriving uncertainty estimates for material flows has a stronger effect on the uncertainty ranges of the resulting plastics flows than the assumptions about the underlying probability distributions. Because these uncertainty estimates originate from data quality evaluation as well as uncertainty characterization, it is crucial to use a well‐defined approach, building on several steps to ensure the consistent translation of the data quality underlying material flow calculations into their associated uncertainties. Although subjectivity is inherent in uncertainty assessment in MFA, the proposed approach is consistent and provides a comprehensive documentation of the choices underlying the uncertainty analysis, which is essential to interpret the results and use MFA as a decision support tool.     

Fuzzy Stochastic Petri Nets for Modeling Biological Systems with Uncertain Kinetic Parameters

Stochastic Petri nets (SPNs) have been widely used to model randomness which is an inherent feature of biological systems. However, for many biological systems, some kinetic parameters may be uncertain due to incomplete, vague or missing kinetic data (often called fuzzy uncertainty), or naturally vary, e.g., between different individuals, experimental conditions, etc. (often called variability), which has prevented a wider application of SPNs that require accurate parameters. Considering the strength of fuzzy sets to deal with uncertain information, we apply a specific type of stochastic Petri nets, fuzzy stochastic Petri nets (FSPNs), to model and analyze biological systems with uncertain kinetic parameters. FSPNs combine SPNs and fuzzy sets, thereby taking into account both randomness and fuzziness of biological systems. For a biological system, SPNs model the randomness, while fuzzy sets model kinetic parameters with fuzzy uncertainty or variability by associating each parameter with a fuzzy number instead of a crisp real value. We introduce a simulation-based analysis method for FSPNs to explore the uncertainties of outputs resulting from the uncertainties associated with input parameters, which works equally well for bounded and unbounded models. We illustrate our approach using a yeast polarization model having an infinite state space, which shows the appropriateness of FSPNs in combination with simulation-based analysis for modeling and analyzing biological systems with uncertain information.     

Expert elicitation and data noise learning for material flow analysis using Bayesian inference

Bayesian inference allows the transparent communication and systematic updating of model uncertainty as new data become available. When applied to material flow analysis (MFA), however, Bayesian inference is undermined by the difficulty of defining proper priors for the MFA parameters and quantifying the noise in the collected data. We start to address these issues by first deriving and implementing an expert elicitation procedure suitable for generating MFA parameter priors. Second, we propose to learn the data noise concurrent with the parametric uncertainty. These methods are demonstrated using a case study on the 2012 US steel flow. Eight experts are interviewed to elicit distributions on steel flow uncertainty from raw materials to intermediate goods. The experts' distributions are combined and weighted according to the expertise demonstrated in response to seeding questions. These aggregated distributions form our model parameters' informative priors. Sensible, weakly informative priors are adopted for learning the data noise. Bayesian inference is then performed to update the parametric and data noise uncertainty given MFA data collected from the United States Geological Survey and the World Steel Association. The results show a reduction in MFA parametric uncertainty when incorporating the collected data. Only a modest reduction in data noise uncertainty was observed using 2012 data; however, greater reductions were achieved when using data from multiple years in the inference. These methods generate transparent MFA and data noise uncertainties learned from data rather than pre-assumed data noise levels, providing a more robust basis for decision-making that affects the system.     

Characterization and Simulation of Uncertain Frequency Distributions: Effects of Distribution Choice,Variability, Uncertainty,and Parameter Dependence

We use bootstrap simulation to characterize uncertainty in parametric distributions, including Normal, Lognormal, Gamma, Weibull, and Beta, commonly used to represent variability in probabilistic assessments. Bootstrap simulation enables one to estimate sampling distributions for sample statistics, such as distribution parameters, even when analytical solutions are not available. Using a two-dimensional framework for both uncertainty and variability, uncertainties in cumulative distribution functions were simulated. The mathematical properties of uncertain frequency distributions were evaluated in a series of case studies during which the parameters of each type of distribution were varied for sample sizes of 5, 10, and 20. For positively skewed distributions such as Lognormal, Weibull, and Gamma, the range of uncertainty is widest at the upper tail of the distribution. For symmetric unbounded distributions, such as Normal, the uncertainties are widest at both tails of the distribution. For bounded distributions, such as Beta, the uncertainties are typically widest in the central portions of the distribution. Bootstrap simulation enables complex dependencies between sampling distributions to be captured. The effects of uncertainty, variability, and parameter dependencies were studied for several generic functional forms of models, including models in which two-dimensional random variables are added, multiplied, and divided, to show the sensitivity of model results to different assumptions regarding model input distributions, ranges of variability, and ranges of uncertainty and to show the types of errors that may be obtained from mis-specification of parameter dependence. A total of 1,098 case studies were simulated. In some cases, counter-intuitive results were obtained. For example, the point value of the 95th percentile of uncertainty for the 95th percentile of variability of the product of four Gamma or Weibull distributions decreases as the coefficient of variation of each model input increases and, therefore, may not provide a conservative estimate. Failure to properly characterize parameter uncertainties and their dependencies can lead to orders-of-magnitude mis-estimates of both variability and uncertainty. In many cases, the numerical stability of two-dimensional simulation results was found to decrease as the coefficient of variation of the inputs increases. We discuss the strengths and limitations of bootstrap simulation as a method for quantifying uncertainty due to random sampling error.     

Charging up Battery Recycling Policies: Extended Producer Responsibility for Single‐Use Batteries in the European Union,Canada, and the United States

Extended producer responsibility (EPR) policies have proven effective at raising consumer awareness, expanding waste collection infrastructure, and shifting costs of end‐of‐life (EOL) management from municipalities to stewardship organizations. Yet, such policies have been less successful in advancing waste management programs that ensure a net environmental benefit. This article analyzes how EPR policies for single‐use batteries in the European Union (EU), Canada, and the United States address the environmental costs and benefits of EOL management. Considering these EPR policies is instructive, because single‐use batteries have high collection costs and are of relatively low economic value for waste processors. Without deliberate planning, the environmental burdens of collecting and recycling such batteries may exceed the benefits. This article considers how EPR policies for single‐use batteries integrate performance requirements such as collection rates, recycling efficiencies, and best available techniques. It argues that for such policies to be effective, they need to be extended to address waste collection practices, the life cycle consequences of EOL management, and the quality of recovered materials. Such strategies are relevant to EPR policies for other products with marginal secondary value, including some textiles, plastics, and other types of electronic waste.     

Quantifying spatial classification uncertainties of the historical Wisconsin landscape (USA)

Landscape feature can be classified by creating categories based on aggregation of spatially explicit information. However, many landscape features appear continuous rather than discrete. The aggregation process likely involves loss of information and introduces a variety of uncertainties whose degree and extent may differ spatially. Since landscape classifications have found wide application in e.g. natural resource policies or ecological research, assessments of spatial classification uncertainties are required.
We present a quantitative framework to identify the degree of landscape continuity (fuzziness) and structure (categorization) based on fuzzy classification and offer measures to quantify uncertainties originating from aggregating features into categories. Fuzzy classification is a non-hierarchical, quantitative method of assessing class definitions using degrees of association between features and class. This results in classes which are well defined and compositionally distinct, as well as classes which are less clearly defined but which, to various degrees, share characteristics with some or all classes. The spatial variation in the degree of class definition on the landscape is used to assess classification uncertainties. The two aspects of uncertainty investigated are the degree of association of a feature with the overall class definitions (membership diffusion), and the class-specific degree of association of each pixel on the landscape with each class (membership saturation).
Three classification scenarios, one fuzzy and one discrete, of the historical landscape of Wisconsin (USA) were compared for spatial classification uncertainties. Membership diffusion is highest in topographically heterogeneous environments, or areas characterized by many species occupying similar ecological niches. Classification uncertainties for individual classes show that differentiated species distributions can be identified, not only distribution centers.     

Substance Flow Analysis of Wastes Containing Polybrominated Diphenyl Ethers

The present article examines flows and stocks of Stockholm Convention regulated pollutants, commercial penta‐ and octabrominated diphenyl ether (cPentaBDE, cOctaBDE), on a city level. The goals are to (1) identify sources, pathways, and sinks of these compounds in the city of Vienna, (2) determine the fractions that reach final sinks, and (3) develop recommendations for waste management to ensure their minimum recycling and maximum transfer to appropriate final sinks. By means of substance flow analysis (SFA) and scenario analysis, it was found that the key flows of cPentaBDE stem from construction materials. Therefore, end‐of‐life (EOL) plastic materials used for construction must be separated and properly treated, for example, in a state‐of‐the‐art municipal solid waste (MSW) incinerator. In the case of cOctaBDE, the main flows are waste electrical and electronic equipment (WEEE) and, possibly, vehicles. Most EOL vehicles are exported from Vienna and pose a continental, rather than a local, problem. According to the modeling, approximately 73% of cOctaBDE reached the final sink MSW incinerator, and 17% returned back to consumption by recycling. Secondary plastics, made from WEEE, may thus contain significant amounts of cOctaBDE; however, uncertainties are high. According to uncertainty analysis, the major cause is the lack of reliable values regarding cOctaBDE concentrations in European WEEE categories 3 and 4, including cathode ray tube monitors for computers and televisions. We recommend establishing a new, goal‐oriented data set by additional analyses of waste constituents and plastic recycling samples, as well as establishing reliable mass balances of polybrominated diphenyl ethers’ flows and stocks by means of SFA.     

Climate change,uncertainty, and natural resource management

Climate change and its associated uncertainties are of concern to natural resource managers. Although aspects of climate change may be novel (e.g., system change and nonstationarity), natural resource managers have long dealt with uncertainties and have developed corresponding approaches to decision-making. Adaptive resource management is an application of structured decision-making for recurrent decision problems with uncertainty, focusing on management objectives, and the reduction of uncertainty over time. We identified 4 types of uncertainty that characterize problems in natural resource management. We examined ways in which climate change is expected to exacerbate these uncertainties, as well as potential approaches to dealing with them. As a case study, we examined North American waterfowl harvest management and considered problems anticipated to result from climate change and potential solutions. Despite challenges expected to accompany the use of adaptive resource management to address problems associated with climate change, we conclude that adaptive resource management approaches will be the methods of choice for managers trying to deal with the uncertainties of climate change. © 2010 The Wildlife Society.     

Life cycle assessment of lightweight and end-of-life scenarios for generic compact class passenger vehicles

Goal, Scope and Background  The automotive industry has a long history in improving the environmental performance of vehicles - fuel economy and emission improvements, introduction of recycled and renewable materials, etc. The European Union also aims at improving the environmental performance of products by reducing, in particular, waste resulting from End-of-Life Vehicles (ELVs) for example. The European Commission estimates that ELVs contribute to approximately 1 % of the total waste in Europe [9]. Other European Union strategies are considering more life cycle aspects, as well as other impacts including resource or climate change. This article is summarizing the results of a European Commission funded project (LIRECAR) that aims at identifying the environmental impacts and relevance for combinations of recycling / recovery and lightweight vehicle design options over the whole life cycle of a vehicle - i.e. manufacturing, use and recycling/recovery. Three, independent and scientific LCA experts reviewed the study according to ISO 14040. From the beginning, representatives of all Life Cycle Stakeholders have been involved (European materials & supplier associations, an environmental Non-Governmental Organization, recycler’s association). Model and System Definition  The study compared 3 sets of theoretical vehicle weight scenarios: 1000 kg reference (material range of today’s end-of-life, mid-sized vehicles produced in the early 1990’s) and 2 lightweight scenarios for 100 kg and 250 kg less weight based on reference functions (in terms of comfort, safety, etc.) and a vehicle concept. The scenarios are represented by their material range of a broad range of lightweight strategies of most European car manufacturers. In parallel, three End-of-Life (EOL) scenarios are considered: EOL today and two theoretical extreme scenarios (100% recycling, respectively, 100% recovery of shredder residue fractions that are disposed of today). The technical and economical feasibility of the studied scenarios is not taken into consideration (e.g. 100% recycling is not possible). Results and Discussion  Significant differences between the various, studied weight scenarios were determined in several scenarios for the environmental categories of global warming, ozone depletion, photochemical oxidant creation (summer smog), abiotic resource depletion, and hazardous waste. However, these improvement potentials can be only realized under well defined conditions (e.g. material compositions, specific fuel reduction values and EOL credits) based on case-by-case assessments for improvements over the course of the life cycle. Looking at the studied scenarios, the relative contribution of the EOL phase represents 5% or less of the total life cycle impact for most selected impact categories and scenarios. The EOL technology variations studied do not impact significantly the considered environmental impacts. Exceptions include total waste, as long as stockpile goods (overburden, tailings and ore/coal processing residues) and EOL credits are considered. Conclusions and Recommendations  LIRECAR focuses only on lightweight/recycling, questions whereas other measures (changes in safety or comfort standards, propulsion improvements for CO₂, user behavior) are beyond the scope of the study. The conclusions are also not necessarily transferable to other vehicle concepts. However, for the question of end-of-life options, it can be concluded that LIRECAR cannot support any general recommendation and/or mandatory actions to improve recycling if lightweight is affected. Also, looking at each vehicle, no justification could be found for the general assumption that lightweight and recycling greatly influence the affected environmental dimension (Global Warming Potential or resource depletion and waste, respectively). LIRECAR showed that this general assumption is not true under all analyzed circumstances and not as significant as suggested. Further discussions and product development targets shall not focus on generic targets that define the approach/technology concerned with how to achieve environmental improvement (weight reduction [kg], recycling quota [%]), but on overall life cycle improvement). To enable this case-by-case assessment, exchanges of necessary information with suppliers are especially relevant.     

A Probabilistic Dynamic Material Flow Analysis Model for Chinese Urban Housing Stock

The stock‐driven dynamic material flow analysis (MFA) model is one of the prevalent tools to investigate the evolution and related material metabolism of the building stock. There exists substantial uncertainty inherent to input parameters of the stock‐driven dynamic building stock MFA model, which has not been comprehensively evaluated yet. In this study, a probabilistic, stock‐driven dynamic MFA model is established and China's urban housing stock is selected as the empirical case. This probabilistic dynamic MFA model has the ability to depict the future evolution pathway of China's housing stock and capture uncertainties in its material stock, inflow, and outflow. By means of probabilistic methods, a detailed and transparent estimation of China's housing stock and its material metabolism behavior is presented. Under a scenario with a saturation level of the population, urbanization, and living space, the median value of the urban housing stock area, newly completed area, and demolished area would peak at around 49, 2.2, and 2.2 billion square meters, respectively. The corresponding material stock and flows are 79, 3.5, and 3.3 billion tonnes, respectively. Uncertainties regarding housing stock and its material stock and flows are non‐negligible. Relative uncertainties of the material stock and flows are above 50%. The uncertainty importance analysis demonstrates that the material intensity and the total population are major contributions to the uncertainty. Policy makers in the housing sector should consider the material efficiency as an essential policy to mitigate material flows of the urban building stock and to lower the risk of policy failures.     

物质流分析研究述评

物质流分析方法近年来在循环经济和可持续发展研究领域发展迅速。阐述了物质流分析的定义及其与自然生态系统物质流的区别,着重回顾了该研究方法的发展历程,阐明了物质流分析的主要观点、理论基础、研究思路及研究框架,详细阐译和对比分析了物质流分析的六大类指标及分析方法,并在物质流分析框架的基础上,建立循环经济及可持续发展的评价指标体系,并对物质流分析指标体系和方法学的研究意义及其在环境经济学中的地位进行了客观的评价,进而指出了物质流分析方法的不足之处。对物质流分析在不同层次的应用研究也进行了充分的阐述和分析。对物质流分析今后相关领域的进一步研究予以了讨论和展望。     

Evaluation of habitat suitability index models by global sensitivity and uncertainty analyses: a case study for submerged aquatic vegetation

Habitat suitability index (HSI) models are commonly used to predict habitat quality and species distributions and are used to develop biological surveys, assess reserve and management priorities, and anticipate possible change under different management or climate change scenarios. Important management decisions may be based on model results, often without a clear understanding of the level of uncertainty associated with model outputs. We present an integrated methodology to assess the propagation of uncertainty from both inputs and structure of the HSI models on model outputs (uncertainty analysis: UA) and relative importance of uncertain model inputs and their interactions on the model output uncertainty (global sensitivity analysis: GSA). We illustrate the GSA/UA framework using simulated hydrology input data from a hydrodynamic model representing sea level changes and HSI models for two species of submerged aquatic vegetation (SAV) in southwest Everglades National Park: Vallisneria americana (tape grass) and Halodule wrightii (shoal grass). We found considerable spatial variation in uncertainty for both species, but distributions of HSI scores still allowed discrimination of sites with good versus poor conditions. Ranking of input parameter sensitivities also varied spatially for both species, with high habitat quality sites showing higher sensitivity to different parameters than low‐quality sites. HSI models may be especially useful when species distribution data are unavailable, providing means of exploiting widely available environmental datasets to model past, current, and future habitat conditions. The GSA/UA approach provides a general method for better understanding HSI model dynamics, the spatial and temporal variation in uncertainties, and the parameters that contribute most to model uncertainty. Including an uncertainty and sensitivity analysis in modeling efforts as part of the decision‐making framework will result in better‐informed, more robust decisions.     

Optimal applicator placement in hepatic radiofrequency ablation on the basis of rare data

In this paper, a numerical procedure to determine an optimal applicator placement for hepatic radiofrequency ablation incorporating uncertain material parameters is presented. The main focus is set on the treatment of subjective and rare data-based information. For this purpose, we employ the theory of fuzzy sets and model uncertain parameters as fuzzy quantities. While fuzzy modelling has been established in structural engineering in the recent past, it is novel in biomedical engineering. Incorporating fuzzy quantities within an optimisation task is basically innovative. In our context, fuzzy modelling allows us to determine an optimal applicator placement that maximises the therapy success under the given uncertainty conditions. The applicability of our method is demonstrated by means of an example case.     

Validation-based model selection for 13C metabolic flux analysis with uncertain measurement errors

Accurate measurements of metabolic fluxes in living cells are central to metabolism research and metabolic engineering. The gold standard method is model-based metabolic flux analysis (MFA), where fluxes are estimated indirectly from mass isotopomer data with the use of a mathematical model of the metabolic network. A critical step in MFA is model selection: choosing what compartments, metabolites, and reactions to include in the metabolic network model. Model selection is often done informally during the modelling process, based on the same data that is used for model fitting (estimation data). This can lead to either overly complex models (overfitting) or too simple ones (underfitting), in both cases resulting in poor flux estimates. Here, we propose a method for model selection based on independent validation data. We demonstrate in simulation studies that this method consistently chooses the correct model in a way that is independent on errors in measurement uncertainty. This independence is beneficial, since estimating the true magnitude of these errors can be difficult. In contrast, commonly used model selection methods based on the χ²-test choose different model structures depending on the believed measurement uncertainty; this can lead to errors in flux estimates, especially when the magnitude of the error is substantially off. We present a new approach for quantification of prediction uncertainty of mass isotopomer distributions in other labelling experiments, to check for problems with too much or too little novelty in the validation data. Finally, in an isotope tracing study on human mammary epithelial cells, the validation-based model selection method identified pyruvate carboxylase as a key model component. Our results argue that validation-based model selection should be an integral part of MFA model development.     

Consumption‐based Material Flow Accounting

In 2007, imports accounted for approximately 34% of the material input (domestic extraction and imports) into the Austrian economy and almost 60% of the GDP stemmed from exports. Upstream material inputs into the production of traded goods, however, are not yet included in the standard framework of material flow accounting (MFA). We have reviewed different approaches accounting for these upstream material inputs, or raw material equivalents (RME), positioning them in a wider debate about consumption‐based perspectives in environmental accounting. For the period 1995–2007, we calculated annual RME of Austria's trade and consumption applying a hybrid approach. For exports and competitive imports, we used an environmentally extended input‐output model of the Austrian economy, based on annual supply and use tables and MFA data. For noncompetitive imports, coefficients for upstream material inputs were extracted from life cycle inventories. The RME of Austria's imports and exports were approximately three times larger than the trade flows themselves. In 2007, Austria's raw material consumption was 30 million tonnes or 15% higher than its domestic material consumption. We discuss the material composition of these flows and their temporal dynamics. Our results demonstrate the need for a consumption‐based perspective in MFA to provide robust indicators for dematerialization and resource efficiency analysis of open economies.     

Dynamic modelling under uncertainty: the case of Trypanosoma brucei energy metabolism

Kinetic models of metabolism require detailed knowledge of kinetic parameters. However, due to measurement errors or lack of data this knowledge is often uncertain. The model of glycolysis in the parasitic protozoan Trypanosoma brucei is a particularly well analysed example of a quantitative metabolic model, but so far it has been studied with a fixed set of parameters only. Here we evaluate the effect of parameter uncertainty. In order to define probability distributions for each parameter, information about the experimental sources and confidence intervals for all parameters were collected. We created a wiki-based website dedicated to the detailed documentation of this information: the SilicoTryp wiki (http://silicotryp.ibls.gla.ac.uk/wiki/Glycolysis). Using information collected in the wiki, we then assigned probability distributions to all parameters of the model. This allowed us to sample sets of alternative models, accurately representing our degree of uncertainty. Some properties of the model, such as the repartition of the glycolytic flux between the glycerol and pyruvate producing branches, are robust to these uncertainties. However, our analysis also allowed us to identify fragilities of the model leading to the accumulation of 3-phosphoglycerate and/or pyruvate. The analysis of the control coefficients revealed the importance of taking into account the uncertainties about the parameters, as the ranking of the reactions can be greatly affected. This work will now form the basis for a comprehensive Bayesian analysis and extension of the model considering alternative topologies.     

Assessing the Greenhouse Gas Savings Potential of Extended Producer Responsibility for Mattresses and Boxsprings in the United States

Extended producer responsibility (EPR) legislation in the United States, which currently only exists on the state level, now includes three mattress EPR acts, which intend to shift the financial and operational burden of mattress end‐of‐life (EOL) management away from local and state government. It is important to keep in mind, however, that the original objective behind EPR is to reduce the environmental life cycle impacts of products. This article therefore quantifies the greenhouse gas (GHG) savings potential of mattress and boxspring recycling and reuse in the United States and also discusses labor implications and mattress design issues. We find that all three acts are unlikely to generate redesign incentives, but are expected to dramatically increase mattress collection and recycling. The collection and recycling of all 35 million EOL mattress and boxspring units estimated to reach the end of their lives in the United States every year would generate in the order of 10,000 jobs and GHG savings between 1 and 1.5 million metric tonnes.     

Recycling Rates of Aluminum in the United States

Recycling rates of aluminum are defined in different (sometimes inconsistent) ways and poorly quantified. To address this situation, the definitions and calculation methods of four groups of indicators are specified for the United States: (1) indicators used to measure recycling efficiencies of old aluminum scrap at the end‐of‐life (EOL) stage, including EOL collection rate (CR), EOL processing rate, EOL recycling rate, and EOL domestic recycling rate; (2) indicators used to compare generation or use of new with old scrap, including new to old scrap ratio, new scrap ratio (NSR), and old scrap ratio; (3) indicators used to compare production or use of primary aluminum with secondary aluminum, including four recycling input rates (RIRs); and (4) indicators used to track the sinks of aluminum metal in the U.S. anthroposphere. I find that the central estimate of EOL CR varies between 38% and 65% in the United States from 1980 to 2009 and shares a relatively similar historical trend with the primary aluminum price. The RIR is shown to be significantly reduced if excluding secondary aluminum produced from new scrap resulting from the relatively high NSR. In 2003, a time when approximately 73% of all of the aluminum produced globally since 1950 was considered to still be “in service,” approximately 68% to 69% of all metallic aluminum that had entered the U.S. anthroposphere since 1900 was still in use: 67% in domestic in‐use stock and 1% to 2% exported as scrap. Only 6% to 7% was definitely lost to the environment, although the destination of 25% of the aluminum was unknown. It was either exported as EOL products, was currently hibernating, or was lost during collection.