Applying Fuzzy and Probabilistic Uncertainty Concepts to the Material Flow Analysis of Palladium in Austria

Material flow analysis (MFA) is a widely applied tool to investigate resource and recycling systems of metals and minerals. Owing to data limitations and restricted system understanding, MFA results are inherently uncertain. To demonstrate the systematic implementation of uncertainty analysis in MFA, two mathematical concepts for the quantification of uncertainties were applied to Austrian palladium (Pd) resource flows and evaluated: (1) uncertainty ranges expressed by fuzzy sets and (2) uncertainty ranges defined by normal distributions given as mean values and standard deviations. Whereas normal distributions represent the traditional approach for quantifying uncertainties in MFA, fuzzy sets may offer additional benefits in relation to uncertainty quantification in cases of scarce information. With respect to the Pd case study, the fuzzy representation of uncertain quantities is more consistent with the actual data availability in cases of incomplete databases, and fuzzy sets serve to highlight the effect of uncertainty on resource efficiency indicators derived from the MFA results. For both approaches, data reconciliation procedures offer the potential to reduce uncertainty and evaluate the plausibility of the model results. With respect to Pd resource management, improved formal collection of end‐of‐life (EOL) consumer products is identified as a key factor in increasing the recycling efficiency. In particular, the partial export of EOL vehicles represents a substantial loss of Pd from the Austrian resource system, whereas approximately 70% of the Pd in the EOL consumer products is recovered in waste management. In conclusion, systematic uncertainty analysis is an integral part of MFA required to provide robust decision support in resource management.     

Information Content,Complexity, and Uncertainty in Material Flow Analysis

Material Flow Analysis (MFA) is a useful method for modeling, understanding, and optimizing sociometabolic systems. Among others, MFAs can be distinguished by two general system properties: First, they differ in their complexity, which depends on system structure and size. Second, they differ in their inherent uncertainty, which arises from limited data quality. In this article, uncertainty and complexity in MFA are approached from a systems perspective and expressed as formally linked phenomena. MFAs are, in a graph‐theoretical sense, understood as networks. The uncertainty and complexity of these networks are computed by use of information measures from the field of theoretical ecology. The size of a system is formalized as a function of its number of flows. It defines the potential information content of an MFA system and holds as a reference against which complexity and uncertainty are gauged. Integrating data quality measures, the uncertainty of an MFA before and after balancing is determined. The actual information content of an MFA is measured by relating its uncertainty to its potential information content. The complexity of a system is expressed based on the configuration of each individual flow in relation to its neighboring flows. The proposed metrics enable different material flow systems to be compared to one another and the role of individual flows within a system to be assessed. They provide information useful for the design of MFAs and for the communication of MFA results. For exemplification, the regional MFAs of aluminum and plastics in Austria are analyzed in this article.     

Evaluating the Use of Global Sensitivity Analysis in Dynamic MFA

Dynamic material flow analysis (MFA) provides information about material usage over time and consequent changes in material stocks and flows. In order to understand the effect of limited data quality and model assumptions on MFA results, the use of sensitivity analysis methods in dynamic MFA studies has been on the increase. So far, sensitivity analysis in dynamic MFA has been conducted by means of a one‐at‐a‐time method, which tests parameter perturbations individually and observes the outcomes on output. In contrast to that, variance‐based global sensitivity analysis decomposes the variance of the model output into fractions caused by the uncertainty or variability of input parameters. The present study investigates interaction and time‐delay effects of uncertain parameters on the output of an archetypal input‐driven dynamic material flow model using variance‐based global sensitivity analysis. The results show that determining the main (first‐order) effects of parameter variations is often sufficient in dynamic MFA because substantial effects attributed to the simultaneous variation of several parameters (higher‐order effects) do not appear for classical setups of dynamic material flow models. For models with time‐varying parameters, time‐delay effects of parameter variation on model outputs need to be considered, potentially boosting the computational cost of global sensitivity analysis. Finally, the implications of exploring the sensitivities of model outputs with respect to parameter variations in the archetypical model are used to derive model‐ and goal‐specific recommendations on choosing appropriate sensitivity analysis methods in dynamic MFA.     

Unified Materials Information System (UMIS): An Integrated Material Stocks and Flows Data Structure

Modern society depends on the use of many diverse materials. Effectively managing these materials is becoming increasingly important and complex, from the analysis of supply chains, to quantifying their environmental impacts, to understanding future resource availability. Material stocks and flows data enable such analyses, but currently exist mainly as discrete packages, with highly varied type, scope, and structure. These factors constitute a powerful barrier to holistic integration and thus universal analysis of existing and yet to be published material stocks and flows data. We present the Unified Materials Information System (UMIS) to overcome this barrier by enabling material stocks and flows data to be comprehensively integrated across space, time, materials, and data type independent of their disaggregation, without loss of information, and avoiding double counting. UMIS can therefore be applied to structure diverse material stocks and flows data and their metadata across material systems analysis methods such as material flow analysis (MFA), input‐output analysis, and life cycle assessment. UMIS uniquely labels and visualizes processes and flows in UMIS diagrams; therefore, material stocks and flows data visualized in UMIS diagrams can be individually referenced in databases and computational models. Applications of UMIS to restructure existing material stocks and flows data represented by block flow diagrams, system dynamics diagrams, Sankey diagrams, matrices, and derived using the economy‐wide MFA classification system are presented to exemplify use. UMIS advances the capabilities with which complex quantitative material systems analysis, archiving, and computation of material stocks and flows data can be performed.     

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.     

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.     

Quantitative Evaluation of Data Quality in Regional Material Flow Analysis

A method for quantitative evaluation of data quality in regional material flow analysis (MFA) is presented. The principal idea is that data quality is a multidimensional problem that cannot be judged by individual characteristics such as the data source, given that data from official statistics may not be per se of good quality and expert estimations may not be per se of bad quality, respectively. It appears that MFA data are never totally accurate and may have certain defects that impair the quality of the data in more than one dimension. The concept of MFA information defects is introduced, and these information defects are mathematically formalized as functions of data characteristics. They are quantified on a scale from 0 (no information defect) to 1 (maximum information defect). The proposed method is illustrated in a case study on palladium flows in Austria. A quantitative evaluation of data quality provides opportunities for understanding and assessing MFA results, their a priori information basis, their reliability in decision making, and data uncertainties. It is a formal step toward better reproducibility and more transparency in MFA.     

Urban Metabolism of Paris and Its Region

The article presents the results of a research project aimed at (1) examining the feasibility of material flow analysis (MFA) on a regional and urban scale in France, (2) selecting the most appropriate method, (3) identifying the available data, and (4) calculating the material balance for a specific case. Using the Eurostat method, the study was conducted for the year 2003 and for three regional levels: Paris, Paris and its suburbs, and the entire region. Applying the method on a local scale required two local indicators to be defined in order to take into account the impact of exported wastes on MFA: LEPO, local and exported flows to nature, and DMC_corr, a modified domestic material consumption (DMC) that excludes exported wastes (and imported ones if necessary). As the region extracts, produces, and transforms less material than the country as a whole, its direct material input (DMI) is lower than the national DMI. In all the areas, LEPO exceeds 50% of DMI; in contrast, recycling is very low. The multiscale approach reveals that urban metabolism is strongly impacted by density and the distribution of activities: the dense city center (Paris) exports all of its wastes to the other parts of the region and concentrates food consumption, whereas the agricultural and urban sprawl area consumes high levels of construction materials and fuel. This supports the use of MFA on an urban and regional scale as a basis for material flow management and dematerialization strategies and clearly reveals the important interactions between urban and regional planning and development, and material flows.     

Cape Town’s Metabolism: Insights from a Material Flow Analysis

This work aims to contribute to the number of urban metabolism case studies using a standardized methodology. An economy‐wide material flow analysis (EW‐MFA) was conducted on the Metropolitan Municipality of Cape Town (South Africa) for the year 2013, using the Eurostat framework. The study provides insights into the city's metabolism through various indicators including direct material input (DMI), domestic material consumption (DMC), and direct material output (DMO), among others. In order to report on the uncertainty of the data, a set of data quality indicators originating from the life cycle assessment literature was used. The results show that domestic extraction involves significant quantities of non‐metallic minerals, and that imports consist primarily of biomass and fossil fuels. The role of the city as a regional hub is also made clear from this study and illustrated by large quantities of food and other materials flowing through the city on their way to or from international markets. The results are compared with indicators from other cities and with previous metabolism work done on Cape Town. To fully grasp the impacts of the city's metabolism, more work needs to be done. It will be necessary to understand the upstream impact of local consumption, and consumption patterns should be differentiated on a more nuanced level (taking into account large differences between household income levels as well as separating the metabolism of industry and commerce from residential consumption).     

Material and Energy Flows and Environmental Impacts of the Internet in Switzerland

The Internet leads to material and energy consumption as well as various environmental impacts on both the regional and global scale. Yet, assessments of the Internet's energy consumption and resulting greenhouse gas emissions are still rare, and assessments of material flows and further environmental impacts are virtually non‐existent. This article investigates material flows, the direct energy consumption during the use phase, as well as environmental impacts linked to the service, “Internet in Switzerland.” In our model, the service, Internet in Switzerland, is divided into various Internet participant categories. All devices used to access or provide Internet services are merged in a limited number of equipment families and, as such, included in an inventory of the existing infrastructure (stock). Based on this inventory, a material flow analysis (MFA) is performed, which includes the current stock as well as flows resulting from growth and disposal. The direct energy consumption for the operation of the infrastructure is quantified. Environmental impacts are calculated with a life cycle assessment approach, using the ecoinvent database and the software, SimaPro, applying four different methods. The MFA results in a 2009 stock of 98,100 tonnes. Approximately 4,130 gigawatt hours per year, or 7% of the total Swiss electricity consumption, were used in 2009 to operate the Swiss infrastructure. The environmental impacts caused during the production and use phases vary significantly depending on the assessment method chosen. The disposal phase had mainly positive impacts as a result of material recovery.     

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.     

Material Flows in a Social Context: A Vietnamese Case Study Combining the Materials Flow Analysis and Action‐in‐Context Frameworks

Materials flow analysis (MFA) is one of the central achievements of industrial ecology. One direction in which one can move MFA beyond mere accounting is by putting the material flows in their social context. This “socially extended MFA” may be carried out at various levels of aggregation. In this article, specific material flows will be linked to concrete actors and mechanisms that cause these flows—using the action‐in‐context (AiC) framework, which contains, inter alia, both proximate and indirect actors and factors. The case study site is of Tat hamlet in Vietnam, set in a landscape of paddy fields on valley floors surrounded by steep, previously forested slopes. Out of the aggregate MFA of Tat, the study focuses on material flows associated with basic needs and sustainability. The most important actors causing these material flows are farming households, politicians, traders, and agribusiness firms—of which local politicians turned out to be pivotal. The study shows the value of combining MFA with actor‐based social analysis. MFA achieves the balanced quantification of the physical system, thus helping to pinpoint key processes. Actor‐based analysis adds the causal understanding of what drives these key processes, leading to improved scenarios of the future and the effective identification of target groups and instruments for policy making.     

A Data Characterization Framework for Material Flow Analysis

The validity of material flow analyses (MFAs) depends on the available information base, that is, the quality and quantity of available data. MFA data are cross‐disciplinary, can have varying formats and qualities, and originate from heterogeneous sources, such as official statistics, scientific models, or expert estimations. Statistical methods for data evaluation are most often inadequate, because MFA data are typically isolated values rather than extensive data sets. In consideration of the properties of MFA data, a data characterization framework for MFA is presented. It consists of an MFA data terminology, a data characterization matrix, and a procedure for database analysis. The framework facilitates systematic data characterization by cell‐level tagging of data with data attributes. Data attributes represent data characteristics and metainformation regarding statistical properties, meaning, origination, and application of the data. The data characterization framework is illustrated in a case study of a national phosphorus budget. This work furthers understanding of the information basis of material flow systems, promotes the transparent documentation and precise communication of MFA input data, and can be the foundation for better data interpretation and comprehensive data quality evaluation.     

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。     

A Material Flow Accounting Case Study of the Lisbon Metropolitan Area using the Urban Metabolism Analyst Model

This article describes a new methodological framework to account for urban material flows and stocks, using material flow accounting (MFA) as the underlying method. The proposed model, urban metabolism analyst (UMAn), bridges seven major gaps in previous urban metabolism studies: lack of a unified methodology; lack of material flows data at the urban level; limited categorizations of material types; limited results about material flows as they are related to economic activities; limited understanding of the origin and destination of flows; lack of understanding about the dynamics of added stock; and lack of knowledge about the magnitude of the flow of materials that are imported and then, to a great extent, exported. To explore and validate the UMAn model, a case study of the Lisbon Metropolitan Area was used. An annual time series of material flows from 2003 to 2009 is disaggregated by the model into 28 material types, 55 economic activity categories, and 18 municipalities. Additionally, an annual projection of the obsolescence of materials for 2010–2050 was performed. The results of the case study validate the proposed methodology, which broadens the contribution of existing urban MFA studies and presents pioneering information in the field of urban metabolism. In particular, the model associates material flows with economic activities and their spatial location within the urban area.     

Combining Material Flow Analysis,Life Cycle Assessment,and Multiattribute Utility Theory

Three assessment methods, material flow analysis (MFA), life cycle analysis (LCA), and multiattribute utility theory (MAUT) are systematically combined for supporting the choice of best end‐of‐life scenarios for polyethylene terephthalate (PET) waste in a municipality of a developing country. MFA analyzes the material and energy balance of a firm, a region, or a nation, identifying the most relevant processes; LCA evaluates multiple environmental impacts of a product or a service from cradle to grave; and MAUT allows for inclusion of other aspects along with the ecological ones in the assessment. We first systematically coupled MFA and LCA by defining “the service offered by the total PET used during one year in the region” as the functional unit. Inventory and impacts were calculated by multiplying MFA flows with LCA impacts per kilogram. We used MAUT to include social and economic aspects in the assessment. To integrate the subjective point of view of stakeholders in the MAUT, we normalized the environmental, social, and economic variables with respect to the magnitude of overall impacts or benefits in the country. The results show large benefits for recycling scenarios from all points of view and also provide information about waste treatment optimization. The combination of the three assessment methods offers a powerful integrative assessment of impacts and benefits. Further research should focus on data collection methods to easily determine relevant material flows. LCA impact factors specific to Colombia should be developed, as well as more reliable social indicators.     

Global Patterns and Trends for Non‐Metallic Minerals used for Construction

Despite accounting for almost 50% of global material use, nonmetallic minerals—mostly used for construction of buildings and infrastructure—are the material flow analysis (MFA) category with the highest uncertainty. The main reason for this is incomplete reporting in official national statistics because of ease of availability and the low per‐unit cost of these materials. However, the environmental burden associated with nonmetallic minerals, which include energy use for extraction and transport, land‐use change, and disposal of large amounts of construction demolition waste, call for a thorough understanding of the magnitude of nonmetallic mineral flows. Previous estimates for nonmetallic minerals have used simplistic assumptions. This study aims to increase the precision of nonmetallic mineral accounts at national and global level using consumption of bitumen, bricks, cement, and railways in combination with technical coefficients from the engineering literature to infer the actual yearly consumption of nonmetallic minerals. We estimate the extraction of nonmetallic minerals and provide uncertainty estimates for the new accounts as well as information about consumption by different sectors. Analyzing the evolution of consumption for seven world regions, we find that, in North America and Europe, the consumption of nonmetallic minerals over the past 40 years has followed the growth patterns of population, whereas for all other regions consumption has been closely related to gross domestic product (GDP). A more accurate account of global and country‐by‐country extraction of nonmetallic minerals may provide insights into supply shortages and inform waste management strategies for construction and demolition waste.     

Evaluation of Modeling Approaches to Determine End‐of‐Life Flows Associated with Buildings: A Viennese Case Study on Wood and Contaminants

Dynamic material flow analysis enables the forecasting of secondary raw material potential for waste volumes in future periods, by assessing past, present, and future stocks and flows of materials in the anthroposphere. Analyses of waste streams of buildings stocks are uncertain with respect to data and model structure. Wood construction in Viennese buildings serve as a case study to compare different modeling approaches for determining end‐of‐life (EoL) wood and corresponding contaminant flows (lead, chlorine, and polycyclic aromatic hydrocarbons). A delayed input and a leaching stock modeling approach are used to determine wood stocks and flows from 1950 until 2100. Cross‐checking with independent estimates and sensitivity analyses are used to evaluate the results’ plausibility. In the situation of the given data in the present case study, the delay approach is a better choice for historical observations of EoL wood and for analyses at a substance level. It has some major drawbacks for future predictions at the goods level, though, as the durability of a large number of historical buildings with considerably higher wood content is not reflected in the model. The wood content parameter differs strongly for the building periods and has therefore the highest influence on the results. Based on this knowledge, general recommendations can be derived for analyses on waste flows of buildings at a goods and substance level.