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.     

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.     

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.     

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.     

Linking Material Flow Analysis with Environmental Impact Potential

Technology transition can have significant implications on the evolution of environmental impact potential of disposed electronics over time. Considering technology transition, we quantify the temporal behavior of ecological and human health impact potential from select heavy metals in electronic waste (e‐waste). The case study analyzes product substitution effects in two electronic cohorts from the U.S. market: (1) computers (laptops substituting for desktops) and (2) televisions (flat‐panel liquid crystal displays [LCDs] and plasma displays substituting for cathode‐ray tubes [CRTs]). Quantities of end‐of‐life (EoL) units to year 2030 are forecasted by the unique combination of dynamic material flow analysis, logistic trend analysis, and product lifespan calibration methods. Metal content from EoL units are assessed via a pathway and effect model using USETox? characterization factors to determine the toxicity potential attributed to heavy metal releases into different media (e.g., air, water, and soil) as an indicator of environmental burden. Results show high impact materials such as lead, nickel, and zinc cause changes in human health toxicity potential and copper causes changes in ecological toxicity potential. Effects of dematerialization, such as reduced metal content in laptops over desktops, provide some positive benefits in toxicity potential per product. However, from a market perspective, emerging e‐waste quantities created by increasing per capita penetration rates of electronics and increasing population will offset gains in environmental performance at the product level. The resulting analysis provides guidance on the timing expected for emerging EoL units and an indication of high impact potential materials requiring pollution prevention as product substitution occurs.     

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.     

Applying Ecological Input-Output Flow Analysis to Material Flows in Industrial Systems

This article, continuing with the themes of the companion article, expounds the capabilities of input-output techniques as applied to material flows in industrial systems. Material flows are the primary focus because of their role in directly linking natural and industrial systems and thereby being fundamental components of environmental issues in industrial economies. The specific topic in this article concerns several material flow metrics used to characterize system behavior that are derived from the ecological development of input-output techniques; most notable of these metrics are several measures of material cycling and a measure of the number of processes visited by material while in a system. These metrics are shown to be useful in analyzing the state of material flow systems. Further-more, the metrics are shown to be a central link in connecting input-output flow analysis to synthesis (i.e., the process of using measurements of system behavior to design changes to that system). By connecting the flow metrics to both environmental objectives and controllable aspects of flow models, changes to existing flow systems are synthesized to generate improved system behavior. To bring this pair of articles to a close, several limitations of input-output flow analysis are summarized with the goal of stimulating further interest and research.     

Dynamic Material Flow Analysis for Strategic Construction and Demolition Waste Management in Beijing

Of all materials extracted from the earth's crust, the construction sector uses 50%, producing huge amounts of construction and demolition waste (CDW). In Beijing, presently 35 million metric tons per year (megatonnes/year [Mt/yr]) of CDW are generated. This amount is expected to grow significantly when the first round of mass buildings erected in the 1990s starts to be demolished. In this study, a dynamic material flow analysis (MFA) is conducted for Beijing's urban housing system, with the demand for the stock of housing floor area taken as the driver. The subsequent effects on construction and demolition flows of housing floor area and the concurrent consumption and waste streams of concrete are investigated for Beijing from 1949 and projected through 2050. The per capita floor area (PCFA) is a key factor shaping the material stock of housing. Observations in Beijing, the Netherlands, and Norway indicate that PCFA has a strong correlation with the local gross domestic product (GDP). The lifetime of dwellings is one of the most important variables influencing future CDW generation. Three scenarios, representing the current trend extension, high GDP growth, and lengthening the lifetime of dwellings, are analyzed. The simulation results show that CDW will rise, unavoidably. A higher growth rate of GDP and the consequent PCFA will worsen the situation in the distant future. Prolonging the lifetime of dwellings can postpone the arrival of the peak CDW. From a systematic view, recycling is highly recommended for long‐term sustainable CDW management.     

Material Flow and Ecological Restructuring in China

Environmental problems are closely related to society' processing of materials through the entire economy. Because neither traditional environmental nor economic analytical methods can provide sufficient insight into the physical dimension of economies, this article presents an integrated methodology, combining a substance flow analysis (SFA) approach and an ecological restructuring analysis. This approach is applied to phosphorus (P) in China, one of the most rapidly growing industrializing economies, in order to better understand of the economy's material use and its change over time. A static national SFA model is developed with statistical data from 1996. By tracking the national economy's P flows from origins to destinations, the critical P flows with respect to environmental impacts are identified. Based on the regime of national P flows, this article analyzes the degree of ecological restructuring by dynamically describing the structural changes of related critical P flows over the last two decades with a set of ecological restructuring indicators (ERIs). Finally, some potential and desired changes are discussed, with the goal of ecologizing the national P flow regime; that is, reducing the ecological impact of the national P flow regime. The methodology of this article illustrates its applicability and value for presenting an overall insight into the physical dimensions of national 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.     

An Evaluation of Sources of Uncertainty in a Dredged Material Assessment

The U.S. Army Corps of Engineers and the U.S. Environmental Protection Agency use a four tiered evaluation process to assess the potential for significant impacts from open water disposal of dredged material to the aquatic environment. This tiered approach requires only the appropriate level of analysis to estimate potential chemical and biological effects. Uncertainty is inherent in each tier and can lead to delayed, costly, and potentially inappropriate decisions. This paper discusses sources of uncertainty in the tiered approach with the goal of improving dredged material management decisions. These potential uncertainty sources are common to many dredging projects but might not be applicable to all projects. Although not all uncertainty sources can be quantified, even using the simple scoring procedure described here, they can still contribute significantly to uncertainty in predictions of adverse effects. Of the sources that could be scored and ranked, those identified as most uncertain include trophic transfer, chronic bioassay interpretation, fate and transport model parameter uncertainty, toxicity endpoints based on body burdens, human dose-response models, toxicity of complex mixtures, and estimation of population-level effects. Research directed at these sources of uncertainty will result in improved decision making.     

Uncertainty Analysis in Biofuel Systems

This article evaluates the implications of uncertainty in the life cycle (LC) energy efficiency and greenhouse gas (GHG) emissions of rapeseed oil (RO) as an energy carrier displacing fossil diesel (FD). Uncertainties addressed include parameter uncertainty as well as scenario uncertainty concerning how RO coproduct credits are accounted for (uncertainty due to modeling choices). We have carried out an extensive data collection to build an LC inventory accounting for parameter uncertainty. Different approaches for carbon stock changes associated with converting set‐aside land to rapeseed cultivation have been considered, which result in different values: from ?0.25 t C/ha.yr (carbon uptake by the soil in tonnes per hectare year) to 0.60 t C/ha.yr (carbon emission). Energy renewability efficiency and GHG emissions of RO are presented, which show the influence of parameter versus scenario uncertainty. Primary energy savings and avoided GHG emissions when RO displaces FD have also been calculated: Avoided GHG emissions show considerably higher uncertainty than energy savings, mainly due to land use (nitrous oxide emissions from soil) and land use conversion (carbon stock changes). Results demonstrate the relevance of applying uncertainty approaches; emphasize the need to reduce uncertainty in the environmental life cycle modeling, particularly GHG emissions calculation; and show the importance of integrating uncertainty into the interpretation of results.     

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.     

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).     

The Sankey Diagram in Energy and Material Flow Management

The Sankey diagram is an important aid in pointing up inefficiencies and potential for savings in connection with resource use. This article, the second of a pair, examines the use of Sankey diagrams in operational material flow management. The previous article described the development of the diagram and its use in the past.
Simple Sankey diagrams follow the requirement of conservation of energy or mass and allow a physical view of production systems. Advanced diagrams integrate stocks of materials beside the flows or show the different (ecological) quality of the materials. For the purpose of management, however, a further step is necessary: to illustrate the economic value of the energy and material flows and to use information from cost accounting. The use of flow charts showing added value or the costs of energy and material flows is particularly important for production systems. This article describes examples of each of these uses as well as assumptions that must be taken into account for Sankey diagrams to be used as an effective aid for decision-making in business and public policy.     

The Sankey Diagram in Energy and Material Flow Management

The Sankey diagram is an important aid in identifying inefficiencies and potential for savings when dealing with resources. It was developed over 100 years ago by the Irish engineer Riall Sankey to analyze the thermal efficiency of steam engines and has since been applied to depict the energy and material balances of complex systems. The Sankey diagram is the main tool for visualizing industrial metabolism and hence is widely used in industrial ecology. In the history of the early 20th century, it played a major role when raw materials were scarce and expensive and engineers were making great efforts to improve technical systems. Sankey diagrams can also be used to map value flows in systems at the operational level or along global value chains. The article charts the historical development of the diagrams. After the First World War the diagrams were used to produce thermal balances of production plants for glass and cement and to optimize the energy input. In the 1930s, steel and iron ore played a strategic role in Nazi Germany. Their efficient use was highlighted with Sankey diagrams. Since the 1990s, these diagrams have become common for displaying data in life cycle assessments (LCAs) of products. Sankey diagrams can also be used to map value flows in systems at the operational level or along global value added chains. This article, the first of a pair, charts the historical development. The companion article discusses the methodology and the implicit assumptions of such Sankey diagrams.     

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 General Nonlinear Least Squares Data Reconciliation and Estimation Method for Material Flow Analysis

The extraction, transformation, use, and disposal of materials can be represented by directed, weighted networks, known in the material flow analysis (MFA) community as Sankey or flow diagrams. However, the construction of such networks is dependent on data that are often scarce, conflicting, or do not directly map onto a Sankey diagram. By formalizing the forms of data entry, a nonlinear constrained optimization program for data estimation and reconciliation can be formulated for reconciling data sets for MFA problems where data are scarce, in conflict, do not directly map onto a Sankey diagram, and are of variable quality. This method is demonstrated by reanalyzing an existing MFA of global steel flows, and the resulting analytical solution measurably improves upon their manual solution.