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.     

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.     

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.     

Enhanced Performance of the Eurostat Method for Comprehensive Assessment of Urban Metabolism: A Material Flow Analysis of Amsterdam

Sustainable urban resource management depends essentially on a sound understanding of a city's resource flows. One established method for analyzing the urban metabolism (UM) is the Eurostat material flow analysis (MFA). However, for a comprehensive assessment of the UM, this method has its limitations. It does not account for all relevant resource flows, such as locally sourced resources, and it does not differentiate between flows that are associated with the city's resource consumption and resources that only pass through the city. This research sought to gain insights into the UM of Amsterdam by performing an MFA employing the Eurostat method. Modifications to that method were made to enhance its performance for comprehensive UM analyses. A case study of Amsterdam for the year 2012 was conducted and the results of the Eurostat and the modified Eurostat method were compared. The results show that Amsterdam's metabolism is dominated by water flows and by port‐related throughput of fossil fuels. The modified Eurostat method provides a deeper understanding of the UM than the urban Eurostat MFA attributed to three major benefits of the proposed modifications. First, the MFA presents a more complete image of the flows in the UM. Second, the modified resource classification presents findings in more detail. Third, explicating throughput flows yields a much‐improved insight into the nature of a city's imports, exports, and stock. Overall, these advancements provide a deeper understanding of the UM and make the MFA method more useful for sustainable urban resource management.     

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.     

城市住宅建筑系统流量-存量动态模拟——以北京市为例

地面建筑物的累积与更新是城市化过程的结果与显性特征之一。城市建筑系统在不同层面上与外部环境系统进行着物质能量交换,对这种交互产生的资源压力与环境胁迫的关注,使其成为城市代谢研究领域中的热点问题。系统分析与模拟城市建筑物流量-存量的动态变化过程及其资源环境响应,对于揭示城市建筑系统代谢机理,提高城市总体规划精准性、强化资源系统韧性管理、提升废弃物处置效率等宏观战略具有重要意义。以北京市为例,基于Stella建模平台,构建了城市居民住宅建筑系统流量-存量的动态模拟模型,定量模拟了不同管理情景下钢材需求量与建筑拆除垃圾产生量的变化区间。结果表明:(1)基准情景下,北京住宅建筑新建流量前期增速较快,2005年达到峰值3024.1万m~2,而拆除流量约于2057年达到峰值,拆除面积为2073.14万m~2;城市住宅建筑存量最高值出现在2075年左右,面积为7.51亿m~2;(2)与基准情景相比,如果人均住宅建筑面积提高到45 m~2,从现在到模拟期结束(2019—2100)将增加钢铁需求量3251.65万t;而如果延长住宅建筑寿命至设计值,同期可减少钢铁需求量3022.9万t;(3)基准情景、大面积情景以及长寿命情景下,北京市城镇住宅建筑拆除垃圾峰值产生量分别为0.29亿t、0.39亿t、0.20亿t,政府管理部门应采取有针对性的应对措施,提前做出综合利用和处理处置方案。     

Tracking Construction Material over Space and Time: Prospective and Geo‐referenced Modeling of Building Stocks and Construction Material Flows

Construction material plays an increasingly important role in the environmental impacts of buildings. In order to investigate impacts of materials on a building level, we present a bottom‐up building stock model that uses three‐dimensional and geo‐referenced building data to determine volumetric information of material stocks in Swiss residential buildings. We used a probabilistic modeling approach to calculate future material flows for the individual buildings. We investigated six scenarios with different assumptions concerning per‐capita floor area, building stock turnover, and construction material. The Swiss building stock will undergo important structural changes by 2035. While this will lead to a reduced number in new constructions, material flows will increase. Total material inflow decreases by almost half while outflows double. In 2055, the total amount of material in‐ and outflows are almost equal, which represents an important opportunity to close construction material cycles. Total environmental impacts due to production and disposal of construction material remain relatively stable over time. The cumulated impact is slightly reduced for the wood‐based scenario. The scenario with more insulation material leads to slightly higher material‐related emissions. An increase in per‐capita floor area or material turnover will lead to a considerable increase in impacts. The new modeling approach overcomes the limitations of previous bottom‐up building models and allows for investigating building material flows and stocks in space and time. This supports the development of tailored strategies to reduce the material footprint and environmental impacts of buildings and settlements.     

Urban development and sustainability challenges chronicled by a century of construction material flows and stocks in Tiexi,China

Construction materials are considerable forces of global environmental impacts, but their dynamics vis‐à‐vis urban development are poorly documented, in part because their long lifespans require elusive and sometimes nonexistent decade‐long high‐resolution data. This study analyzes the construction material flow and stock trends that shaped and were shaped by the development, decline, and renewal of the Tiexi district of Shenyang, a microcosm of China's urban transformations since the early 20th century. Chronicling building‐by‐building the material flows and stock accumulations involved in the buildup of this area, we shed light on the physical resource context of its socioeconomic history. We find that 42 million tonnes of construction materials were needed to develop the Tiexi district from 1910 to 2018, and 18 million tonnes of material outflows were generated by end‐of‐life building demolition. However, over 55% of inflows and 93% of outflows occurred since 2002 during a complete redevelopment of the district. Only small portions of end‐of‐life materials could have been reused or recycled because of temporal and typological mismatches of supply and demand and technical limitations. Our analysis reveals a dramatic decrease in median building lifetimes to as low as 6 years in the early 21st century. These findings contribute to the discussion of long‐term environmental efficiency and sustainability of societal development through construction and reflect on the challenges of urban renewal processes not only in China but also in other developing and developed countries that lost (or may lose) their traditional economic base and restructure their urban forms. This article met the requirements for a Silver/Silver JIE data openness badge described at http://jie.click/badges .     

Material Flows and Economic Growth in Developing China

The concept of sustainable development concerns not only the natural environment but also human societies and economies. The method of economy‐wide materials flow accounting and analysis (EW‐MFA) is internationally recognized as a valuable tool for studying the physical dimensions of economies. EW‐MFA has been carried out in many industrialized countries, but very little work has been done for developing China; this article can be regarded as one of the first attempts to study China's economy in terms of materials flows. In this article we have compiled materials flow accounts for China during the time series 1990 to 2002 and derived indicators associated with international comparison. Results show that the annual material consumption of China's economy continuously increased except for a slump around 1998, whereas the material efficiency exhibited a three‐phase trend reflecting different macropolicies of the Eighth, Ninth, and Tenth Five‐Year Plans implemented by the central government. Based on this experience with EW‐MFA for China, suggestions for methodology development and further research are given for improving EW‐MFA as a more effective tool for environmental management.     

Regional material flow accounts for China: Examining China's natural resource use at the provincial and national level

Over the last three decades, China has experienced the most dynamic economic development lifting living standards and resulting in fast‐growing use of natural resources. In the past, the focus has been on national MFA accounts which do not do justice to the second largest economy, home to 19% of the world population and having 30% of global material use. In this research, we calculate material extraction for China at the regional level during 1995–2015 using the most recent available statistical data and applying the most up‐to‐date international calculation methods. In particular, we combine a bottom‐up and top‐down approach for constructing the dataset of China's economically used Domestic Extraction (DEU) in an integrated way. This approach also improves the Chinese national material flow accounts and allows us to present a reliable database of DE of materials for China to date. Our new dataset provides the basis for calculating material footprints and environmental impacts of China's regions. The dataset enables us to evaluate regional resource efficiency trends in China. We find that during the past two decades, China's material use has grown strongly from 11.7 billion tonnes in 1995 to 35.4 billion tonnes in 2015. Material use has accelerated between 2000 and 2010 but slowed down between 2010 and 2015 reflecting the economic contraction caused by the Global Financial Crisis which reduced the global demand for China's manufacturing and a reorientation of China's economic policy settings toward quality of growth. Unsurprisingly, different regions play different roles in the supply chain of materials, achieving different economic performances resulting in very diverse material efficiency outcomes. This information is important to allow for a targeted policy approach to increase resource efficiency, reduce environmental impacts of resource use, and grow wellbeing in China with large positive implications for global sustainability. This study provides the basis for the development of relevant resource management policies for different regions in the future.     

Using spatially explicit commodity flow and truck activity data to map urban material flows

To analyze and promote resource efficiency in urban areas, it is important to characterize urban metabolism and particularly, material flows. Material flow analysis (MFA) offers a means to capture the dynamism of cities and their activities. Urban‐scale MFAs have been conducted in many cities, usually employing variants of the Eurostat methodology. However, current methodologies generally reduce the study area into a “black box,” masking details of the complex processes within the city's metabolism. Therefore, besides the aggregated stocks and flows of materials, the movement of materials—often embedded in goods or commodities—should also be highlighted. Understanding the movement and dispersion of goods and commodities can allow for more detailed analysis of material flows. We highlight the potential benefits of using high‐resolution urban commodity flows in the context of understanding material resource use and opportunities for conservation. Through the use of geographic information systems and visualizations, we analyze two spatially explicit datasets: (1) commodity flow data in the United States, and (2) Global Positioning System‐based commercial vehicle (truck) driver activity data in Singapore. In the age of “big data,” we bring advancements in freight data collection to the field of urban metabolism, uncovering the secondary sourcing of materials that would otherwise have been masked in typical MFA studies. This brings us closer to a consumption‐based, finer‐resolution approach to MFA, which more effectively captures human activities and its impact on urban environments.     

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.     

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.     

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.     

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.     

Probabilistic Assessment of Industrial Synergistic Systems

A probability‐based method is presented for assessing the reliability of synergistic systems and their ability to cope with the uncertainties often associated with two of a company's main types of activities: those carried out by the manufacturing department, and those carried out by the storage department. This method is based on a model focusing on the dynamic simulation of synergistic flows in terms of the mass balance. It differs from previous material flow analysis tools, which do not take into account the temporary failures occurring at the companies involved and the resulting loss of production capacity. The failure events occurring at any of the companies in a synergistic system may result in various levels of synergy failure and a short supply of resources for other companies. We therefore propose to identify the main factors responsible for a lack of synergy. We developed a dynamic stock simulation model for assessing the reliability of synergistic systems as well as that of the individual companies of a system before and after a synergy is set up. We first confirm the validity of this model by comparing the results with those based on the binomial theorem in system reliability analysis, and we then apply the model to the case of an industrial system. We conclude that companies involved in a synergistic system will inevitably be exposed to a higher risk of resource shortage because of the unsteady synergistic and outsourcing flows on which they depend. More efficient stock management methods would prevent the occurrence of the risks often associated with synergistic flows.     

Estimating Materials Stocked by Land‐Use Type in Historic Urban Buildings Using Spatio‐Temporal Analytical Tools

The construction industry is an important contributor to urban economic development and consumes large volumes of building material that are stocked in cities over long periods. Those stocked spaces store valuable materials that may be available for recovery in the future. Thus quantifying the urban building stock is important for managing construction materials across the building life cycle. This article develops a new approach to urban building material stock analysis (MSA) using land‐use heuristics. Our objective is to characterize buildings to understand materials stocked in place by: (1) developing, validating, and testing a new method for characterizing building stock by land‐use type and (2) quantifying building stock and determining material fractions. We conduct a spatial MSA to quantify materials within a 2.6‐square‐kilometer section of Philadelphia from 2004 to 2012. Data were collected for buildings classified by land‐use type from many sources to create maps of material stock and spatial material intensity. In the spatial MSA, the land‐use type that returned the largest footprint (by percentage) and greatest (number) of buildings were civic/institutional (42%; 147) and residential (23%; 275), respectively. The model was validated for total floor space and the absolute overall error (n = 46; 20%) in 2004 and (n = 47; 24%) in 2012. Typically, commercial and residential land‐use types returned the lowest overall error and weighted error. We present a promising alternative method for characterizing buildings in urban MSA that leverages multiple tools (geographical information systems [GIS], design codes, and building models) and test the method in historic Philadelphia.