Mixed-unit hybrid life cycle assessment applied to the recycling of construction materials

Teh, Soo Huey; Wiedmann, Thomas; Moore, Stephen Article Mixed-unit hybrid life cycle assessment applied to the recycling of construction materials Journal of Economic Structures Provided in Cooperation with: Pan-Pacific Association of Input-Output Studies (PAPAIOS) Suggested Citation: Teh, Soo Huey; Wiedmann, Thomas; Moore, Stephen (2018) : Mixed-unit hybrid life cycle assessment applied to the recycling of construction materials, Journal of Economic Structures, ISSN 2193-2409, Springer, Heidelberg, Vol. 7, Iss. 13, pp. 1-25, https://doi.org/10.1186/s40008-018-0112-4 This Version is available at: https://hdl.handle.net/10419/194918 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. 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If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. https://creativecommons.org/licenses/by/4.0/ Mixed‑unit hybrid life cycle assessment applied to the recycling of construction materials Soo Huey Teh1*, Thomas Wiedmann1,2 and Stephen Moore1 Abstract The construction industry contributes around 18% of greenhouse gas emissions, 40% of depletion of natural resources, and 25% of wastes globally. To reduce these impacts, construction industries can adopt low-carbon alternatives for construction materials and waste minimisation strategies, including the recycling of construction and demolition waste. However, a comprehensive understanding of the full life cycle carbon profile of low-carbon and recyclable construction materials is required to accurately assess the efficacy of decarbonisation strategies in the built environment. Despite recent progress in hybrid life cycle assessment (hybrid LCA) methods, some weaknesses remain with respect to the inherent uncertainty relating to price variations and aggregated sectors that are unable to provide detailed waste-specific information in hybrid LCA. Furthermore, attributional, hybrid LCA for a functional unit does not reflect the actual, economy-wide physical flows of materials in a real economy. In this study, a mixed-unit hybrid LCA approach based on a combination of process life cycle inventory, input–output, and material flow data is used to model the economy-wide potential use of recycled construction materials in Australia. A comparison between methods of life cycle emissions of geopolymer concrete revealed that the mixed-unit hybrid LCA approach produced a more accurate and Australian-specific result. The usefulness of the proposed mixed-unit IO model is demonstrated through quantifying the cradle-togate embodied emissions of recycled construction materials and by-products utilised in concrete and steel sectors in Australia. The results yield a 1% reduction when recycled concrete aggregate completely replaces natural aggregate in both ordinary Portland cement and geopolymer concrete. Greenhouse gas emissions reduction of 30% is quantified for geopolymer concrete using recycled concrete aggregate compared with ordinary Portland cement concrete utilising natural aggregate and 43% is estimated for electric arc furnace route using iron and steel scrap compared with basic oxygen furnace route. The method merges physical and monetary units of industrial systems related to low-carbon alternatives and recycled construction materials to enable the calculations of embodied carbon with improved accuracy. The results of this study can help inform decarbonisation strategies in the built environment sector. Keywords: Hybrid LCA, Material flow analysis (MFA), Mixed-unit input–output model, Concrete, Steel, Recycling, Recycled concrete aggregate Open Access © The Author(s) 2018. This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creat iveco mmons .org/licen ses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. RESEARCH Teh et al. Economic Structures (2018) 7:13 https://doi.org/10.1186/s40008‑018‑0112‑4 *Correspondence: soohuey[email protected].au 1 Sustainability Assessment Program (SAP), School of Civil and Environmental Engineering, University of New South Wales, Sydney, NSW 2052, Australia Full list of author information is available at the end of the article Page 2 of 25 Teh et al. Economic Structures (2018) 7:13 1 Background The construction industry is responsible for about 18% of greenhouse gas emissions (GHGE), 40% of depletion of natural resources, and 25% of wastes worldwide (Ding 2014; Yu etal. 2017). Hence, it is important that sustainable alternatives and practices are explored in order to reduce the industry’s environmental impacts. Low-carbon alternatives for construction materials and waste minimisation strategies, such as recycling construction and demolition (C&D) waste, are ways for the construction industry to contribute to a more sustainable development in the wider economy. However, only a comprehensive quantitative evaluation can assess whether the actual benefits of lowcarbon and recycled construction materials are realised if the whole life cycle of all processes is taken into account. One of the main disadvantages of input–output table (IOT) is that it does not consider the end-of-life phase, which includes recycling (Nakamura and Nansai 2016). Therefore, a suitable method is needed to capture the full, economywide carbon profile of alternative construction materials and practices. The most widely used method to assess the impacts of construction materials is process-based life cycle assessment (LCA). LCA provides material-specific data, but suffers from having an incomplete system boundary, which can lead to an underestimation of the impacts assessed (Crawford 2008; Crawford etal. 2018; Wan Omar etal. 2014). On the other hand, input–output analysis (IOA) offers economy-wide system boundary completeness, based on inter-industry monetary flow data, but has limitations relating to price variability, sector aggregation, and the lack of material-specific data (Dixit 2017; Säynäjoki etal. 2017). In a pure monetary input–output table (MIOT), the direct and indirect interdependencies of sectors are captured in monetary units (e.g. dollars), and monetary data are readily available. However, as the prices of physical products vary between different sectors, the assumption that the physical quantity transactions between sectors are proportional to their monetary values leads to an erroneous allocation of environmental impacts in the standard MIOT model (Lenzen 2000; Vendries Algarin etal. 2016). Furthermore, economies of scale means that the per unit monetary value of production outputs vary with their physical quantities (Bullard and Herendeen 1975). The issue of price variability and heterogeneity can be avoided by replacing monetary units with actual material production and consumption flows in physical units. Physical input–output table (PIOT) is expressed only in physical units, showing the flows of physical products across sectors and their interaction with the environment, as well as accounting for waste flows (Giljum and Hubacek 2004; Hoekstra and van den Bergh 2006). PIOT is superior in representing mass flows, but is instead unable to capture monetary flows in the economy for service-based sectors. Data to construct PIOT are also often scarce, conflicting, and labour-intensive, resulting in a limited number of aggregated tables constructed with low resolution (Altimiras-Martin 2014). In addition to price variation, other reasons for differences in results between MIOT and PIOT approaches, as identified by Giljum and Hubacek (2004), Liang and Zhang (2013) and Weisz and Duchin (2006), include assumptions relating to unique sector prices, level of sector aggregation, the models’ capability of dealing with service sectors, accounting for waste, and estimating residential consumption. Hence, the selection of an appropriate unit type is an important decision. Page 3 of 25 Teh et al. Economic Structures (2018) 7:13 A ‘hybrid’ approach constitutes either the utilisation of both monetary and physical units, or the amalgamation of process and input–output (IO) data (Suh etal. 2004). With the former, information gaps can be completed with the more suitable data type (e.g. unit mass for raw materials, and unit dollar for services). The removal of the price conversion step in calculating commodity costs also reduces inaccuracies associated with methods of approximation. In terms of data, process-based life cycle inventory (LCI) data provide specific sector resolution and physical ingredients for the manufacture of a product, whilst economy-wide IO data capture the monetary inter-dependencies of industry sectors, and economy-wide material flow analysis (MFA) data capture the total quantity of material flow used by an industry sector. The confluence between combining physical with monetary units and amalgamating process LCI, IO and material flow data (Fig.1) leads to the mixed-unit hybrid life cycle assessment (MU-hLCA) framework (Hawkins etal. 2007; Hawkins 2007), which can be seen as equivalent to the integrated hLCA in Suh (2004). The advantage of this method is that it combines the precision of process-based MFA, the detailed sector resolution of LCI, and the comprehensiveness of IOA. It adds missing upstream supply chains to the MFA component, whilst also resolving the price variability issue by introducing physical units into the model. By doing so, all phases of the product life cycle including the use, disposal, and recycling stages can be examined. To date, various mixed-unit input–output table (MUIOT) approaches have been used, including extended IO models for energy and emissions (Mayer and Flachmann 2011), integrated hLCA to track heavy metals sectors (Hawkins etal. 2007; Hawkins 2007), ecological IOA in modelling energy and material flows through an industrial system (Bailey etal. 2004), and multi-layered MUIOT, which utilises the mass, energy, and monetary layers (Majeau-Bettez etal. 2016). Fig. 1 Relationship of methods and proposed methodology of MU-hLCA (EW-MFA economy-wide material flow analysis) Page 4 of 25 Teh et al. Economic Structures (2018) 7:13 In this study, MU-hLCA is employed in a case study on geopolymer concrete (GPC) and compared to published results from process-based and IO-based methods. The usefulness of MU-hLCA is demonstrated through its application in modelling an economywide potential use of recycled construction materials in Australia. The proposed model merges physical and monetary units of industrial systems related to construction materials and their recycled substitute products in order to enable the calculations of embodied carbon and the tracking of material flows with improved accuracy. The results of this study will help inform decarbonisation strategies in the built environment sector. This study is the first to model recycled construction materials on an economy-wide scale via a MU-hLCA framework that utilises Australian-specific LCI and IO databases. The novel features of this study are described in the implementation steps taken as outlined below: 1. modelling recycled construction materials in a MU-hLCA framework; 2. economy-wide scale use of specific materials; 3. use of an Australian-specific database (AusLCI 2015) in the MU-hLCA framework; and. 4. applying a decomposition method adapted from Wiedmann (2017) for process, products and industries. The concrete case study from Teh etal. (2017) is advanced in this study to quantify life cycle embodied emissions of concrete and steel incorporating recycled construction materials. 2 Application of MU‑hLCA 2.1 Aim and scope The goal of this study is primarily to compare the results of the MU-hLCA approach with that of input–output-based hybrid life cycle assessment (IO-hLCA) and LCA using GPC as a case study. Secondly, the application and the usefulness of the MU-hLCA method are demonstrated through modelling the use of recycled materials and by-products in concrete and steel, specifically via comparing the carbon footprint intensity (CFI) in the following scenarios: 1. Ordinary Portland cement concrete (with natural aggregate) versus ordinary Portland cement concrete (with recycled concrete aggregate); 2. Fly ash-based GPC concrete (with fly ash, slag, and natural aggregate) versus fly ashbased GPC concrete (with fly ash, slag and recycled concrete aggregate); and 3. Basic oxygen furnace steel versus electric arc furnace steel (with iron and steel scrap). 2.2 Modelling recycling of construction waste It is vital for the construction industry to recycle C&D waste as the industry consumes a large amount of natural resources. C&D waste accounts for approximately 40% (19 Mt) of total solid waste in Australia (47 Mt) (Hyder Consulting 2011a, b; Productivity Commission 2006), of which 45% is disposed to the landfill and 55% is recycled (Hyder Consulting 2011a) (in 2008–2009). The C&D waste stream is composed of masonry materials Page 5 of 25 Teh et al. Economic Structures (2018) 7:13 (concrete, bricks, asphalt, etc.), metals (steel, non-ferrous, etc.), organics, paper and cardboard, plastics, glass, leather and textiles, tyres and hazardous substances. Masonry waste (which includes concrete, bricks, asphalt, etc.) constitutes the greatest proportion in the C&D waste stream at 89% (15 Mt), but only 62% (9 Mt) is currently being recycled (Hyder Consulting 2011a). In comparison, metal constitutes only around 20% (951 kt) of the C&D waste, but has a much higher recycling rate of 87% (831 kt) (Hyder Consulting 2011a). Concrete and steel are chosen as case studies because (1) global aggregate production has doubled from 21 to 40 Gt (from 2007 to 2014) leading to a growing issue of scarcity in landfill space and aggregates (Tam etal. 2018; Torres etal. 2017), and (2) concrete and steel are major contributors to climate change, each accounting for 56 and 6% of 30 Mt of Australian building products produced annually (Miller etal. 2015). Impacts of construction materials that incorporate recycled products have been assessed with process-based LCA in many studies, for example concrete (Braunschweig etal. 2011; Kleijer etal. 2017; Knoeri etal. 2013; Marinković etal. 2010; Shan etal. 2017; Tošić etal. 2015) and steel (Burchart-Korol 2013; Norgate etal. 2007; Olmez etal. 2016). At the same time, few studies have used IO-based hLCA (Choi etal. 2011; Kucukvar etal. 2014; Teh etal. 2017; Wijayasundara etal. 2017a). Kucukvar etal. (2014) combined process-based LCA with IOA to assess the onsite impacts and higher order supply chain impacts of recycling, incineration and landfilling of C&D waste. Wijayasundara etal. (2017a) compared the embodied energy of recycled aggregate concrete and natural aggregate concrete in Australia using the IO-based hLCA method. Choi etal. (2011) analysed the economic impact of localised e-waste recycling using an extended IO model by (2) reconstructing the IO table and satellite environmental account to describe e-waste recycling sectors, and (2) monetising physical e-waste flows in a consistent manner. So far, no studies have been conducted using MU-hLCA to model an economy-wide scale of recycled construction materials. One of the disadvantages specific to the application of recycled products in the IO framework is that waste is an aggregated sector, which is labelled “Waste collection, treatment and disposal services” in the Australian IOT, and does not provide detailed, waste-specific information. Recycling industries and their accompanying products are also not captured in detail and are sometimes represented as part of the main waste sector (Choi etal. 2011). Secondly, waste services are rendered in monetary units, i.e. in terms of service fees, and do not equate to the actual physical amounts of waste generated (Liang and Zhang 2013). A framework known as the waste input–output (WIO) model, which specifically analyses waste types and treatment flows using an extended IOT method, was developed by Nakamura and Kondo (2002, 2009). Using the WIO model, Nakamura and Kondo (2006) evaluated the economic and environmental impacts of waste treatment methods and recycling of electrical appliances in Japan. The WIO model was subsequently applied to calculate waste multipliers and footprints in Australia (Fry etal. 2016; Lenzen and Reynolds 2014; Reynolds etal. 2014). This study offers an alternative approach, by using a MU-hLCA framework where waste flows can be represented in physical units, and specific products including, but not limited to, by-products and recycled products can be distinguished without necessitating the manual disaggregation of IO sectors. This is because the process table already contains disaggregated flows with the resolution to Page 6 of 25 Teh et al. Economic Structures (2018) 7:13 represent specific products. The framework is compiled by integrating a comprehensive Australian-specific LCI database with a monetary supply-and-use table (SUT) in order to draw information regarding the physical input recipe and the associated emissions of producing a single unit of waste or recycled product. Physical MFA data (that represent the quantity of economy-scale flows of waste or recycled products) replace monetary flows in the IOT in the downstream cut-off (Cd) matrix (refer to S3.1.3) that connects the IOT with the LCI database, and the missing upstream inputs are filled in the upstream cut-off (Cu) matrix (refer to S3.1.4) (Fig.5). 2.3 Application of MU‑hLCA to recycled construction materials 2.3.1 Recycled concrete aggregate and by‑products used in concrete In Australia, concrete waste is recycled as recycled aggregate (RA) and is most commonly utilised in unbound applications including road sub-base, pavement, drainage and landscaping (Net Balance 2012). Concrete waste recycling process involves crushing, sorting and screening that are processed on the construction site itself or transported to other processing sites to produce RA (Tam 2009; Tam etal. 2018). Recycled concrete aggregate (RCA) is RA that is used to replace natural (coarse) aggregate (NA) in new concrete. Of the 6 Mt of concrete waste that is recycled in Australia (Net Balance 2012), only a small amount (0.5 Mt) is used in bound applications as RCA, such as in new low-strength, non-structural concretes (CCAA 2008; Tam etal. 2018). The potential of replacing NA with RCA in fly ash-based GPC has also been studied by Galvin and Lloyd (2011) and Limbachiya etal. (2012), who recommended that a 30% RCA replacement rate is ideal to avoid adversely impacting the physical properties of fly ashbased GPC. By-products such as fly ash (FA) and ground granulated blast furnace slag (GGBFS) from coal and steel production that would otherwise be sent to the landfill can also be reused as ingredients for GPC, which is a low-carbon alternative to ordinary Portland cement (OPC) concrete. Although there are numerous benefits of recycling C&D waste (for instance the reduction of waste disposal and thus the need for landfill space, and the conservation of natural resources), currently no RCA is used in high-grade structural applications in Australia (Wijayasundara etal. 2016) and only 1% is used worldwide (Tošić etal. 2015). This is because the utilisation of RCA in structural applications is currently not supported by existing Australian technical and performance standards (Berndt etal. 2013; CSIRO 2002; Tam etal. 2013), and it is known to have lower quality and strength attributes compared to NA (Senaratne etal. 2017). Hence, there is potential for increasing the use of RCA in concrete applications, such as in the production of different concrete types and in various structural applications. 2.3.2 Iron and steel scrap Metal waste, which is predominantly composed of steel, has a high recycling rate because of its profitability. In Australia, more than 50% of metal scrap is exported (ACOR 2015; Corder etal. 2015). According to Hyder Consulting (2009), around 89% (2.5 Mt) of steel waste is recycled and the rest (0.3 Mt) is landfilled in Australia. A study by Golev and Corder (2016) showed that although the reported metal scrap recycling rates are high, Page 7 of 25 Teh et al. Economic Structures (2018) 7:13 i.e. in the range of 80–90%, actual metal recycling rates are estimated to be below 70% when losses and end-of-life product dissipation are taken into account. The recycling of steel involves the processes of collecting, sorting, shredding, and separating different types of scrap metals (Björkman and Samuelsson 2014). In the electric arc furnace (EAF) fabrication of steel products, iron and steel scrap are the primary inputs (ACOR 2015), along with pig iron, electricity as the main energy source, coke, fluxes, and cryogenic gases. The proportion of scrap utilisation in steel-making is estimated to be around 93% for EAF and around 5% in blast furnace–basic oxygen furnace (BF-BOF) production in Australia (Energetics 2012). The production of steel via the EAF route increased from 18% in 2003 to 23% in 2012 in Australia (Golev and Corder 2016) and currently accounts for approximately 30% of global steel output (Yellishetty etal. 2010). A detailed study by Golev and Corder (2016) estimated that currently in Australia, crude steel comprises 20–30% of recycled material content. Emissions can be reduced by recycling steel, given that it is a versatile material that can be recycled and reused efficiently and indefinitely. 3 Method and data 3.1 Construction of MU‑hLCA matrix The MU-hLCA method connects a top-down IOT matrix with a comprehensive bottom-up LCI process matrix through the Cu and Cd matrices (Fig.5). 3.1.1 Process matrix and process environmental extension matrix The process matrix and the process-based environmental extension matrix utilised in this study (referred to as the “process system” and indicated by the subscript p) is from the Australian Life Cycle Inventory database (AusLCI 2015), which captures 4463 processes and four selected environmental extensions, including direct emissions of methane (CH4), nitrous oxide (N2O), carbon dioxide (CO2), and carbon dioxide equivalents (CO2-eq). The preparation of the process coefficient matrix (I–Ap) for Eq.3 comprises a procedure that normalises the LCI database and the associated environmental extension data to reflect the physical functional flows and the process emission intensity (fp) for the fabrication of one functional unit of product or process. The consumption of inputs is represented as negative values whilst the production of outputs are represented as positive values. Finally, the normalised functional units (represented by a series of 1s) are placed on the diagonal of the process coefficient matrix. 3.1.2 Input–output matrix and associated GHGE extension matrix The IO matrix and IO environmental extension matrix used for this study (referred to as the “IO system” and indicated by the subscript IO) is the same two-region SUT framework used in Teh etal. (2017), which in turn is based on data from the Australian Industrial EcologyVirtual Laboratory (IELab) (Lenzen etal. 2014, 2017). It comprises 341 industries, 345 products, 26 rest-of-the-world (RoW) sectors, and four environmental extensions representing direct emissions of CH4, N2O, CO2, and CO2-eq for the year 2009. The SUT framework is adopted because of its ability to capture industry and product detail that enables the allocation of co-products to an industry. Page 8 of 25 Teh et al. Economic Structures (2018) 7:13 The IO sectors listed below have been disaggregated and replaced with specific process data in previous studies (Teh etal. 2017; McIlvin 2015) to reflect detailed construction materials, which are represented as blue lines and dots in Fig.5: •Cement: Selected IO data in the “Cement” sector was replaced with process data for OPC (Teh etal. 2017). •Concrete: The “Ready-mixed concrete and mortar” sector was disaggregated into six types of concrete with varying incorporated proportions of OPC, FA and GGBFS as well as FA-based and GGBFS-based GPC (Teh etal. 2017). The concrete types used in this study are “100% OPC 50MPa concrete” (referred to as OPC concrete) in S4.2.1 and “90% FA/10% GGBFS Geopolymer 50MPa concrete” (referred to as FAbased GPC) in S4.1 and S4.2.1. •Steel: The “Other steel and iron manufacturing” sector was disaggregated into “BOF steel manufacturing” and “EAF steel manufacturing” sectors (McIlvin 2015). The technology coefficient (AIO) matrix is first calculated by dividing each transaction (xij) in the T matrix by the total industry output (Xj), and then deducted from the identity (I) matrix to yield the “I–AIO matrix” for Eq.3. The direct intensity multipliers (fIO) vector expressed in kg of GHGE per $ (AUD) is obtained by dividing the IO environmental extension vector (Ej) by the total industry output (Xj). 3.1.3 Downstream cut‑off matrix The Cd matrix captures the physical amount of products produced by the processes that are distributed to the IO system (Peters and Hertwich 2006; Suh 2006). The Cd matrix is derived by “dividing the annual sales of functional flow—in physical units that are relevant to each functional flow—by the production of each total commodity.” (Suh 2004, p. 459) For instance, this study models the material flow of RCA replacing NA, which is labelled “Gravel” in the IO system, in the production of concrete. This is done by allocating the total annual physical amount of RCA, which is used by the “100% OPC 50MPa concrete” column sector in the background IO system, to the “Recycling brick rubble and concrete, at plant/AU U” row in the process system, and then dividing it by the annual sales of the concrete sector. This calculation step produces the corresponding cell input a C d hj (Eq.1) and is expressed in kg/$, which represents the technology coefficient of RCA (Fig.2). Subsequently, aIO ij (Eq.2), which is the sales coefficient of product row “Gravel” in the “100% OPC 50MPa concrete” industry sector column in the use table is adjusted (set to zero in this case) to avoid double counting (Fig.2). Adjustments in the final demand vectors need to be made accordingly. However, since there is no final demand for RCA or gravel, no further corrections were necessary in this case. (1) a Cd hj = xC d hj X IO j Page 15 of 25 Teh et al. Economic Structures (2018) 7:13 other upstream supply chains is given in Fig.7. The emissions of FA increased from 7 kgCO2-eq/m3 (when only the Cd matrix is populated with data) to 26 kgCO2-eq/m3 (when both the Cu and Cd matrices are populated with data). In contrast, FA contribution was 109 kgCO2-eq/m3 due to its high average price, with a sensitivity range of −32 to +10% based on variances in FA market prices in the previous IO-hLCA study (Teh etal. 2017). Via economic allocation, emissions were assigned according to monetary values between electricity generation and FA based on estimated prices. FA was introduced in the IOT by creating new columns and rows because it was not represented in the IOT. Although upstream impacts were accounted for, the IOT sectors were aggregated and the results were sensitive to price variability. Contributions of Na2SiO3 and NaOH calculated using MU-hLCA (119 and 61 kgCO2-eq/m3) are higher compared to using IO-hLCA (57 and 32 kgCO2-eq/m3) and LCA (45 and 34 kgCO2-eq/m3) (shown in Fig.6). It is noted that consistently across all methods, Na2SiO3 and NaOH are identified as the main contributors of GHGE in the production of GPC. Other studies also confirm that either Na2SiO3 or NaOH is the biggest emitter in GPC production (Davidovits 2015; Habert etal. 2011; McLellan etal. 2011). This study has incorporated all modifications involving the conversion of Na2SiO3 and NaOH from dry weight into 45% wt solution form for feedstock. The higher value of MU-hLCA is due to the use of Australian-specific data (e.g. local energy mix) and the selection of specific chemical production methods from AusLCI. For Na2SiO3 production in Australia, the furnace process is used to melt sodium Fig. 7 Contributions of upstream and downstream cut-off matrix for FA-based geopolymer concrete production using MU-hLCA Page 16 of 25 Teh et al. Economic Structures (2018) 7:13 carbonate and silica sand to produce solid Na2SiO3, which is then dissolved in water to produce liquor Na2SiO3 (Turner and Collins 2013). Production of NaOH is done via the chlor-alkali process, whereby electrolysis converts sodium chloride brine solution to chlorine and NaOH (Turner and Collins 2013). The membrane cell production method is employed by primary NaOH manufacturing companies in Australia (CoogeeChemicals 2016; Orica 2016). Habert and Ouellet-Plamondon (2016) discussed that the energy mix employed in the NaOH production influences the environmental impact data more than the type of production (e.g. membrane cell). Specific methods for the production of “Sodium silicate, furnace liquor, 37% in H2O, at plant/RER U/AusSD U” and “Sodium hydroxide, 50% in H2O, membrane cell, at plant/RER U/AusSD U” are employed from AusLCI in the process system. The previous IO-hLCA study (Teh etal. 2017) has a few gaps, namely that chemical types were aggregated in the “Other basic chemical manufacturing” industry sector, specific diluted chemical solutions could not be distinguished and their conversion to monetary units was problematic. Missing upstream inputs from “Other basic chemical manufacturing” and other upstream supply chains to Na2SiO3 and NaOH in the Cu matrix contributed to an increase of 38 and 11 kgCO2-eq/m3, respectively (Fig.7). It should be noted that the Na2SiO3 and NaOH processes in AusLCI are derived from the “shadow database”, meaning that whilst some significant processes such as those relating to energy and transport are Australian data, the remaining inputs are from the international Ecoinvent database. Hence, there is room for data improvement to achieve more accurate results. In conclusion, the hLCA methodologies led to a higher GHGE of 100–114% compared to the LCA approach due to their system boundary completeness. Although the results of the hLCA methods vary only slightly (18 kgCO2-eq/m3), MU-hLCA yields a more accurate outcome due to (1) the high resolution and country-specific process and IO data available, (2) economy-wide material flow data, (3) the utilisation of physical units to represent by-products and specific chemical products, and (4) an economy-wide system boundary. The drawbacks of IO-hLCA, including the price variability issue, proportionality and homogeneity assumptions, and aggregated sectors, could be addressed with MU-hLCA. 4.2 Application for recycled materials 4.2.1 Recycled concrete aggregate in OPC and GPC The goal of this study is to assess the hypothetical maximum emission reduction achievable using MU-hLCA if 100% of NA (gravel) is replaced with RCA for both GPC and OPC concretes. In this study, concrete types using NA is referred to as “NA concrete,” and concrete types using RCA as a replacement for NA is referred to as “RCA concrete”. The actual amount of RCA used in Australian concrete is guided by the H155:2002 guideline (CSIRO 2002). An environmental impact study of cradle-to-gate embodied emissions of RCA compared with NA concrete using hLCA has not yet been done. LCA studies have reported varying CFI of 320–343 kgCO2-eq/m3 for 100% RCA concrete (Marinković etal. 2017) and 271 kgCO2-eq/m3 for 30% RCA concrete (Kleijer etal. 2017). From this study, the CFI for 100% RCA OPC concrete in the Australian context is higher (516 kgCO2-eq/ m3) as it includes emissions from upstream processes (Fig.8). However, side-by-side Page 17 of 25 Teh et al. Economic Structures (2018) 7:13 comparisons with LCA studies are difficult because there are too many different variables to be considered, such as country of study, mix proportions used for concrete, concrete strength class, RCA replacement rate, and transportation distance. The only hLCA study, of which the authors are aware, by Wijayasundara etal. (2017a) evaluated the embodied energy of RCA compared with NA concrete using IO-hLCA, and concluded that the difference is between −1 and +2%. The 100% replacement of RCA for NA for both GPC and OPC concretes yields a GHGE reduction of 1%. The differences are only due to NA production, given that the compared concrete types are the same, i.e. they use equal amounts of cement. Moreover, the contribution of NA is small to begin with (2% in OPC and 4% in GPC) compared to that of other processes in the life cycle emissions of concrete production. Previous LCA studies evaluating the environmental impacts of RCA concrete compared to NA concrete have produced similar or marginally higher results. An LCA study by Kleijer etal. (2017), which compared product-specific concretes of the same strength, concluded that RCA concrete yields a 2% GHGE reduction. Braunschweig etal. (2011) and Knoeri etal. (2013) found that RCA concrete, including higher cement content, has produced similar GHGE with NA concrete using LCA. Knoeri etal. (2013) also considered the avoided impacts of C&D waste disposal and steel recycling that led to a reduction of other environmental impacts, favouring the use of RCA. A few LCA studies established that RCA transportation types and distances influence the environmental impact of RCA concrete. Marinković etal. (2010) concluded that environmental impact of 100% RCA concrete (with some additional cement) and Fig. 8 CFIs of geopolymer and OPC concrete, with either natural aggregate or 100% recycled concrete aggregate Page 18 of 25 Teh et al. Economic Structures (2018) 7:13 NA concrete are the same if the transport distance of RCA are less than that of NA, but impacts of RCA concrete are larger when their transport distances are the same. An LCA study by Shan etal. (2017) recommended the use of local (Singaporean) RCA due to the higher environmental impact from the country’s reliance on imported material, including NA. For this study, AusLCI physical data for RCA assumes that no transportation to the recycling plant is included because the end-of-waste phase is considered to be at the concrete recycling plant. For NA, the IO system uses an average distance calculation to account for local road transportation. The versatility of MU-hLCA is demonstrated in this study by modelling RCA flows in physical units. Although GHGE reduction in RCA concrete is relatively small, using RCA has other benefits including reducing both waste being landfilled and the depletion of natural resources. A maximum potential GHGE reduction of 30% is achieved with RCA GPC compared to NA OPC concrete, as cement production is the largest contributor to environmental impact in OPC concrete. Apart from the environmental indicator, Wijayasundara etal. (2017b) and Tošić etal. (2015) conducted combined assessments to include other criteria such as social, financial and technical aspects for a more complete comparison. 4.2.2 Iron and steel scrap in EAF steel Embodied emissions of iron and steel scrap used in BOF and EAF steel are modelled using MU-hLCA to assess the possible reductions in GHGE. Based on this, the CFI produced is 1.51 kgCO2-eq per kg of BOF steel (Fig.9). The largest source of GHGE is from the iron and steel manufacturing process, which accounts for 64% of total emissions. For EAF steel, the CFI produced is 0.87 kgCO2-eq/kg (Fig.9). Electricity generation (predominantly based on coal power in Australia) is the largest emitter in the EAF route, contributing to 35% of emissions. It is noted that this represents a cradle-to-gate analysis of producing a unit kg of crude steel, and therefore does not account for the processes thereafter to produce various finished steel products. In comparison, CFI in the range of 1.46–1.65 kgCO2-eq/kg was verified by Environmental Product Declarations (EPDs) for Australian-specific steel products manufactured by BlueScope (with 25% total recycled content) via the integrated steel-making method (BF-BOF route) (Australasian EPD 2015a, b, c). These values are in line with the results produced by this study, which does not take into account the last stage of steel processing. BlueScope reported that an increase of end-of-life recycling rate from the current 89 to 100% can achieve a 12% reduction in global warming potential (GWP), and emphasised the significance of reuse and recycling (Australasian EPD 2015a, b, c). WSA (2015) reported a GHGE of 1.8–1.9 kg CO2 per kg of crude steel cast (for 2012–2014), that reflects the sustainability performance of more than 50% of global steel production. An LCA study by Burchart-Korol (2013) provided a mass allocation breakdown of GHGE for crude steel production, and reported emissions of 1.7 kgCO2-eq/kg for BOF steel and 0.76 kgCO2-eq/kg for EAF steel. The environmental benefit of scrap usage is evident in the 43% GHGE reduction via the EAF route compared with that of the BOF route (Fig.9). According to WSA (2015), steel scrap recycling can conserve approximately 1400kg of iron ore, 740kg of coal, and 120kg of limestone for every tonne of steel scrap used. In terms of energy, Yellishetty Page 19 of 25 Teh et al. Economic Structures (2018) 7:13 etal. (2011) described the use of iron and scrap steel as beneficial from both economic and environmental perspectives because the products have already been refined and require minimal energy for additional processing. The proportion of EAF steel currently stands at 22.8% in Australia and has the potential to increase, considering that approximately 1.7 Mt of steel scrap is currently exported from Australia for external processing (Energetics 2012). However, the EAF route is dependent on the availability of iron and steel scrap, which can be limited due to the majority of steel remaining in use for long periods of time and some steel products are reused directly as new products without recycling or re-melting (Yellishetty etal. 2011). It should be noted that the two types of steel are not fully substitutable yet due to their difference in physical properties (Warrian 2016). However, the intention of this application study is to show that it is possible to assess the potential emission reduction using the MU-hLCA method when a recycled component is used. 5 Assumptions and limitations Factors that influence the accuracy of the results: •The MU-hLCA method is useful for modelling recycled and by-products when the LCI (process system) contains the recycling unit process information (including GHGE in this case), and this recycling information is either aggregated or not represented in the IO system. Fig. 9 CFIs of BOF and EAF steel, with the use of iron and steel scrap Page 20 of 25 Teh et al. Economic Structures (2018) 7:13 •Due to the lack of Australian steel industry data, the bottom-up process data replaced in the disaggregated steel sectors in the IO system is based on Ecoinvent data employed in a study by McIlvin (2015). •AusLCI contains verified Australian unit processes, but where data is limited, it is populated with the shadow database modified from Ecoinvent data. Whilst some energy and transport-related processes are replaced with Australian data, better analysis can be conducted when more Australian-specific data are available. •This study only assesses GHGE (CO2-eq) as the single indicator. Other indicators are neglected in this study, but it is important to note that other impacts of C&D recycling such as the release of other waste and the use of energy, as well as financial, social and technical implications are also important. •The MU-hLCA method is the most advanced form of hLCA methodology, but is also known to be laborious, as well as data and time intensive. To overcome this, Crawford etal. (2017) proposed the automation of integrating IO and process data. 6 Conclusion Recycling studies are usually assessed using LCA and are not considered in the context of the wider economy. There are inherent problems in representing waste and recycled material flows in MIOT because they are either aggregated or not included, and the market value of waste can be little to none. In this study, the Australian economy-wide potential use of recycled construction materials is modelled via the MU-hLCA method, using a combination of LCI, IO and material flow data. By combining physical units of industrial systems representing construction and recycled materials with monetary units of the IO economic sectors, the model enables a more precise computation of embodied carbon and improves the accuracy of tracking material flows. Using GPC as a case study, CFI results derived from MU-hLCA are 5 and 114% higher than IO-hLCA and LCA. MU-hLCA produces more accurate results as it utilises an economy-wide system boundary and Australian process-specific physical flows. Disadvantages of IO-hLCA relating to price variability, assumptions of proportionality, and homogeneity, and the aggregation of sectors could be addressed using MU-hLCA. To demonstrate the flexibility and practicality of MU-hLCA, it is applied in case studies on recycled construction materials and by-products. By using MU-hLCA, all phases including the use, disposal and recycling stages can be represented in physical units, allowing specific products such as RCA, FA, GGBFS, NaOH, Na2SiO3, and iron and steel scrap to be applied in this study without altering the aggregated IOT sectors. GHGE reductions of (1) 1% is observed when 100% of RCA replace NA (gravel) in both GPC and OPC concrete, (2) 30% can potentially be achieved with RCA GPC compared to NA OPC concrete, and (3) 43% is achieved via the EAF route compared to the BOF route. A comprehensive assessment of life cycle emissions of green and alternative materials using MU-hLCA is useful to inform the policies and best practices adopted in the Australian construction industry, making it an essential tool for the successful implementation of these principles by policymakers and the government. A method to capture the physical flows of waste and recycled materials can be done with the WIO model, provided that detailed waste data is available. Whilst MU-hLCA Page 21 of 25 Teh et al. Economic Structures (2018) 7:13 does the same, it is not limited to waste and recycled materials. The high resolution of LCI processes in the process system of the MU-hLCA framework allows for the flexibility of other specific products or processes (e.g. specific chemicals) to be assessed. Another important application of MU-hLCA is in studying circular economy that promotes the sustainable concept of make-use-reuse-recycle (Stahel 2016; Weisz etal. 2015), as it enables the modelling of life cycle emissions when virgin materials are substituted by recycled materials in an economy-wide scale. Circular economy can bring substantial benefits from the environmental, social, and economic points of view, where employment, welfare and gross domestic product could be enhanced (Breene 2016; McKinsey and Company 2016). China has recently decided to disallow imports of foreign waste from countries including Australia (Lasker etal. 2017). To overcome the negative implications of this new policy and to reduce reliance on external providers of waste management services, Australia needs to harness the benefits of reusing and recycling, and reinvigorate local recycling initiatives and transition to a circular economy. MU-hLCA contributes towards the efforts of quantifying the environmental benefits of recycled materials, and can potentially analyse the socioeconomic benefits as well, provided suitable satellite data are available. Recommendations for future research include to further explore the usefulness of the downstream cut-off matrix for economy-wide scenario analysis, and for an assessment on potential GHGE reduction in the built environment. Various products not limited to construction materials such as recycling glass, paper, plastic and metals should be explored. Abbreviations GHGE: greenhouse gas emissions; C&D: construction and demolition; IOT: input–output table; LCA: life cycle assessment; IOA: input–output analysis; MIOT: monetary input–output table; PIOT: physical input–output table; IO: input–output; LCI: life cycle inventory; MFA: material flow analysis; MU-hLCA: mixed-unit hybrid life cycle assessment; hLCA: hybrid life cycle assessment; MUIOT: mixed-unit input–output table; GPC: geopolymer concrete; EW-MFA: economy-wide material flow analysis; IO-hLCA: input–output-based hybrid life cycle assessment; CFI: carbon footprint intensity; WIO: waste input– output; SUT: supply-and-use table; Cd: downstream cut-off matrix; Cu: upstream cut-off matrix; RA: recycled aggregate; RCA : recycled concrete aggregate; NA: natural (coarse) aggregate; OPC: ordinary Portland cement; FA: fly ash; GGBFS: ground granulated blast furnace slag; EAF: electric arc furnace; BF-BOF: blast furnace–basic oxygen furnace; CH4: methane; N2O: nitrous oxide; CO2: carbon dioxide; CO2-eq: carbon dioxide equivalents; AusLCI: Australian Life Cycle Inventory; IELab: Australian Industrial Ecology Virtual Laboratory; RoW: rest-of-the-world; NaOH: sodium hydroxide; Na2SiO3: sodium silicate; SI: supplementary information; mMUH: total impact multipliers; VA: value added; FD: final demand; EPDs: Environmental Product Declarations; GWP: global warming potential. Authors’ contributions SHT wrote the manuscript in consultation with TW and SM. All authors read and approved the final manuscript. Author details 1 Sustainability Assessment Program (SAP), School of Civil and Environmental Engineering, University of New South Wales, Sydney, NSW 2052, Australia. 2 Integrated Sustainability Analysis (ISA), School of Physics A28, The University of Sydney, Sydney, NSW 2006, Australia. Acknowledgements The authors would like to thank Man Yu from UNSW Sydney for her help in the construction of the upstream cut-off matrix. The authors are grateful to Shinichiro Nakamura for his discussions regarding the WIO model. Additional file Additional file 1. Additional information on “Mixed-unit hybrid life cycle assessment applied to the recycling of construction materials”. Page 22 of 25 Teh et al. Economic Structures (2018) 7:13 Competing interests The authors declare that they have no competing interests. Author Thomas Wiedmann is also an Editor of the Journal of Economic Structures. Availability of data and materials The process and input–output datasets employed in this article are available from AusLCI (http://www.auslc i.com.au/) and IELab (https ://ielab .info), respectively. Ethics approval and consent to participate Not applicable. Funding This research is funded by the CRC for Low Carbon Living Ltd supported by the Cooperative Research Centres program, an Australian Government initiative (Project ID RP2007). Publisher’s Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Received: 20 December 2017 Accepted: 1 June 2018 References ABARES (2010) Australian commodity statistics 2010. Department of Agriculture and Water Resources, Canberra, Australia. http://www.agric ultur e.gov.au/abare s/publi catio ns/displ ay?url=http://143.188.17.20/anrdl /DAFFS ervic e/ displ ay.php?fid=pe_abare s9900 1762.xml. Accessed 15 Jan 2016 ABS (2013) Waste account, Australia, experimental estimates. Australian Bureau of Statistics, ACT, Australia. http://www. abs.gov.au/AUSST ATS/[email protected]/Detai lsPag e/4602.0.55.00520 13?OpenD ocume nt. Accessed 11 April 2016 ACOR (2015) Australian scrap market analysis. Australian Council of Recycling, Australia. http://www.acor.org.au/uploa ds/2/1/5/4/21549 240/acor_scrap _marke t_analy sis.pdf. Accessed 5 July 2017 Altimiras-Martin A (2014) Analysing the structure of the economy using physical input–output tables. Econ Syst Res 26:463–485. https ://doi.org/10.1080/09535 314.2014.95063 7 AusLCI (2015) The Australian life cycle inventory database initiative. Australian Life Cycle Assessment Society, Australia. http://alcas .asn.au/AusLC I/index .php/Datas ets/Mater ials. Accessed 30 July 2015 Australasian EPD (2015a) Environmental product declaration for steel—hot rolled coil. BlueScope Steel Limited, Melbourne, Australia. http://www.steel .com.au/artic les/envir onmen tal-produ ct-decla ratio ns. Accessed 15 May 2016 Australasian EPD (2015b) Environmental product declaration for steel—welded beams and columns. BlueScope Steel Limited, Melbourne, Australia. http://www.steel .com.au/artic les/envir onmen tal-produ ct-decla ratio ns. Accessed 15 May 2016 Australasian EPD (2015c) Environmental product declaration for XLERPLATE steel. BlueScope Steel Limited, Melbourne, Australia. http://www.steel .com.au/artic les/envir onmen tal-produ ct-decla ratio ns. Accessed 15 May 2016 Bailey R, Allen JK, Bras B (2004) Applying ecological input–output flow analysis to material flows in industrial systems: part I: tracing flows. J Ind Ecol 8:45–68 Berndt ML, Sanjayan J, Foster S, Castel A (2013) Pathways for overcoming barriers to implementation of low CO2 concrete. CRC For Low Carbon Living. http://www.lowca rbonl iving crc.com.au/sites /all/files /publi catio ns_file_attac hment s/rp100 4_low_carbo n_concr ete_repor t.pdf. Accessed 10 April 2016 Björkman B, Samuelsson C (2014) Chapter 6—recycling of steel. In: Handbook of recycling. Elsevier, Boston, pp 65–83. https ://doi.org/10.1016/b978-0-12-39645 9-5.00006 -4 Braunschweig A, Kytzia S, Bischof S (2011) Recycled concrete: environmentally beneficial over virgin concrete? Paper presented at the life cycle management conference 2011—towards life cycle sustainability management, Berlin, Germany, 28–31 Aug 2011 Breene K (2016) Can the circular economy transform the world’s number one consumer of raw materials? World Economic Forum https ://www.wefor um.org/agend a/2016/05/can-the-circu lar-econo my-trans form-the-world -s-numbe r-one-consu mer-of-raw-mater ials/. Accessed 10 April 2018 Brunner PH, Rechberger H (2004) Practical handbook of material flow analysis. Int J Life Cycle Assess 9:337–338 Bullard CW, Herendeen RA (1975) The energy cost of goods and services. Energ Policy 3:268–278 Burchart-Korol D (2013) Life cycle assessment of steel production in Poland: a case study. J Clean Prod 54:235–243. https ://doi.org/10.1016/j.jclep ro.2013.04.031 CCAA (2008) Use of recycled aggregates in construction. Cement Concrete & Aggregates Australia, NSW, Australia. http:// www.ccaa.com.au. Accessed 17 May 2017 Cencic O, Rechberger H (2008) Material flow analysis with software STAN. Environ Eng Manag J 18:3 Choi T, Jackson RW, Leigh NG, Jensen CD (2011) A baseline input—output model with environmental accounts (IOEA) applied to e-waste recycling. Int Reg Sci Rev 34:3–33. https ://doi.org/10.1177/01600 17610 38545 3 CoogeeChemicals (2016) Chlor-alkali plant in Kwinana, WA. http://www.cooge e.com.au/Our-Busin esses /Chemi calsManuf actur ing/Manuf actur ing-Facil ities /Chlor -Alkal i-Plant -in-Kwina na,-WA. Accessed 3 June 2016 Corder GD, Golev A, Giurco D (2015) “Wealth from metal waste”: translating global knowledge on industrial ecology to metals recycling in Australia. Miner Eng 76:2–9. https ://doi.org/10.1016/j.minen g.2014.11.004 Crawford RH (2008) Validation of a hybrid life-cycle inventory analysis method. J Environ Manag 88:496–506. https ://doi. org/10.1016/j.jenvm an.2007.03.024 Page 23 of 25 Teh et al. Economic Structures (2018) 7:13 Crawford RH, Bontinck P-A, Stephan A, Wiedmann T (2017) Towards an automated approach for compiling hybrid life cycle inventories. Procedia Eng 180:157–166. https ://doi.org/10.1016/j.proen g.2017.04.175 Crawford RH, Bontinck P-A, Stephan A, Wiedmann T, Yu M (2018) Hybrid life cycle inventory methods—a review. J Clean Prod 172:1273–1288. https ://doi.org/10.1016/j.jclep ro.2017.10.176 CSIRO (2002) HB 155-2002: guide to the use of recycled concrete and masonry materials. Commonwealth Scientific and Industrial Research Organisation (CSIRO), Australia Davidovits J (2015) False values on CO2 emission for geopolymer cement/concrete published in scientific papers. Geopolymer Institute, Saint-Quentin, France. http://www.geopo lymer .org/libra ry/techn ical-paper s/false -co2-value s-publi shed-in-scien tific -paper s/. Accessed 17 Jan 2016. Technical Paper #24 Ding GKC (2014) 3—life cycle assessment (LCA) of sustainable building materials: an overview. In: Pacheco-Torgal F, Cabeza LF, Labrincha J, Magalhães Ad (eds) Eco-efficient construction and building materials. Woodhead Publishing, pp 38–62. https ://doi.org/10.1533/97808 57097 729.1.38 Dixit MK (2017) Embodied energy analysis of building materials: an improved IO-based hybrid method using sectoral disaggregation. Energy 124:46–58. https ://doi.org/10.1016/j.energ y.2017.02.047 Energetics (2012) Steel stewardship forum—Australian steel chain footprint project. Energetics, North Sydney, NSW, Australia. file:///C:/Users/z5033911/Downloads/media-File-SSF_Australian_Steel_Chain_Footprint_FINAL_v402. pdf. Accessed 10 May 2017 Fry J, Lenzen M, Giurco D, Pauliuk S (2016) An Australian multi-regional waste supply-use framework. J Ind Ecol 20:1295– 1305. https ://doi.org/10.1111/jiec.12376 Galvin B, Lloyd N (2011) Fly ash based geopolymer concrete with recycled concrete aggregate. Paper presented at the proceedings of the CONCRETE 2011 Conference, Perth, WA, Australia, 12 Oct 2011 Giljum S, Hubacek K (2004) Alternative approaches of physical input–output analysis to estimate primary material inputs of production and consumption activities. Econ Syst Res 16:301–310. https ://doi.org/10.1080/09535 31042 00023 9383 Golev A, Corder G (2016) Modelling metal flows in the Australian economy. J Clean Prod 112:4296–4303. https ://doi. org/10.1016/j.jclep ro.2015.07.083 Grant T (2015) Life cycle inventory of cement & concrete produced in Australia. Life Cycle Strategies Pty Ltd, Melbourne, Australia. www.dropb ox.com/s/yrldi moqif emikh /Cemen t%20con crete %20LCI %20Rep ort%20F1_3.pdf?dl=0. Accessed 30 July 2015 Habert G, Ouellet-Plamondon C (2016) Recent update on the environmental impact of geopolymers. RILEM Tech Lett 1:17–23 Habert G, d’Espinose de Lacaillerie JB, Roussel N (2011) An environmental evaluation of geopolymer based concrete production: reviewing current research trends. J Clean Prod 19:1229–1238. https ://doi.org/10.1016/j.jclep ro.2011.03.012 Hawkins TR (2007) A mixed-unit input–output model for life cycle assessment: development, uncertainty, and application. Ph.D. Dissertation, Carnegie Mellon University Hawkins T, Hendrickson C, Higgins C, Matthews HS, Suh S (2007) A mixed-unit input–output model for environmental life-cycle assessment and material flow analysis. Environ Sci Technol 41:1024–1031. https ://doi.org/10.1021/es060 871u Hoekstra R, van den Bergh JCJM (2006) Constructing physical input–output tables for environmental modeling and accounting: framework and illustrations. Ecol Econ 59:375–393. https ://doi.org/10.1016/j.ecole con.2005.11.005 Hyder Consulting (2009) Waste ferrous metal in Australia—estimate of steel disposed to landfill. Hyder Consulting, North Sydney, Australia. http://steel .org.au/media /File/Hyder _Repor t_Austr alian _Steel _in_Landfi llFI NAL.pdf. Accessed 3 May 2017 Hyder Consulting (2011a) Construction and demolition waste status report, management of construction and demolition waste in Australia. Hyder Consulting, Encycle Consulting & Sustainable Resource Solutions, Melbourne, Australia. http://www.envir onmen t.gov.au/syste m/files /resou rces/323e8 f22-1a8a-4245-a09c-00664 4d3bd 51/files / const ructi on-waste .pdf. Accessed 29 May 2017 Hyder Consulting (2011b) Waste and recycling in Australia 2011: incorporating a revised method for compiling waste and recycling data. Department of Sustainability, Environment, Water, Population and Communities, ACT, Australia. http://www.envir onmen t.gov.au/prote ction /natio nal-waste -polic y/publi catio ns/waste -and-recyc ling-austr alia-2011. Accessed 28 May 2017 Kleijer AL, Lasvaux S, Citherlet S, Viviani M (2017) Product-specific life cycle assessment of ready mix concrete: comparison between a recycled and an ordinary concrete. Resour Conserv Recycl 122:210–218. https ://doi.org/10.1016/j. resco nrec.2017.02.004 Knoeri C, Sanyé-Mengual E, Althaus H-J (2013) Comparative LCA of recycled and conventional concrete for structural applications. Int J Life Cycle Assess 18:909–918. https ://doi.org/10.1007/s1136 7-012-0544-2 Kucukvar M, Egilmez G, Tatari O (2014) Evaluating environmental impacts of alternative construction waste management approaches using supply-chain-linked life-cycle analysis. Waste Manag Res 32:500–508. https ://doi. org/10.1177/07342 42x14 53645 7 Lasker P, Goloubeva J, Birtles B (2017) China’s ban on foreign waste leaves Australian recycling industry eyeing opportunities. http://www.abc.net.au/news/2017-12-10/china -ban-on-forei gn-rubbi sh-leave s-recyc ling-indus try-in-amess/92431 84. Accessed 11 Dec 2017 Lenzen M (2000) Errors in conventional and input–output-based life-cycle inventories. J Ind Ecol 4:127 Lenzen M, Reynolds CJ (2014) A supply-use approach to waste input–output analysis. J Ind Ecol 18:212–226. https ://doi. org/10.1111/jiec.12105 Lenzen M, Geschke A, Wiedmann T, Lane J, Anderson N, Baynes T, Boland J, Daniels P, Dey C, Fry J, Hadjikakou M, Kenway S, Malik A, Moran D, Murray J, Nettleton S, Poruschi L, Reynolds C, Rowley H, Ugon J, Webb D, West J (2014) Compiling and using input–output frameworks through collaborative virtual laboratories. Sci Total Environ 485–486:241–251. https ://doi.org/10.1016/j.scito tenv.2014.03.062 Page 24 of 25 Teh et al. Economic Structures (2018) 7:13 Lenzen M, Geschke A, Malik A, Fry J, Lane J, Wiedmann T, Kenway S, Hoang K, Cadogan-Cowper A (2017) New multiregional input–output databases for Australia—enabling timely and flexible regional analysis. Econ Syst Res 29:275–295. https ://doi.org/10.1080/09535 314.2017.13153 31 Leontief W (1970) Environmental repercussions and the economic structure: an input–output approach. The review of economics and statistics 262–271 Liang S, Zhang T (2013) Investigating reasons for differences in the results of environmental, physical, and hybrid input– output models. J Ind Ecol 17:432–439. https ://doi.org/10.1111/jiec.12010 Limbachiya M, Meddah MS, Ouchagour Y (2012) Use of recycled concrete aggregate in fly-ash concrete. Constr Build Mater 27:439–449. https ://doi.org/10.1016/j.conbu ildma t.2011.07.023 Majeau-Bettez G, Wood R, Hertwich EG, Strømman AH (2016) When do allocations and constructs respect material, energy, financial, and production balances in LCA and EEIO? J Ind Ecol 20:67–84. https ://doi.org/10.1111/ jiec.12273 Marinković S, Radonjanin V, Malešev M, Ignjatović I (2010) Comparative environmental assessment of natural and recycled aggregate concrete. Waste Manag 30:2255–2264. https ://doi.org/10.1016/j.wasma n.2010.04.012 Marinković S, Dragaš J, Ignjatović I, Tošić N (2017) Environmental assessment of green concretes for structural use. J Clean Prod 154:633–649. https ://doi.org/10.1016/j.jclep ro.2017.04.015 Mayer H, Flachmann C (2011) Extended input–output model for energy and greenhouse gases. Federal Statistical Office (Statistisches Bundesamt), Environmental-Economic Accounting, Wiesbaden, Germany. https ://www.desta tis.de/ EN/Publi catio ns/Speci alize d/Envir onmen talEc onomi cAcco untin g/Exten dedIn putOu tputM odel.pdf?__blob=publi catio nFile Accessed 13 Feb 2017 McIlvin K (2015) Low carbon alternatives for steel in Australia’s construction industry. Honours Thesis, The University of New South Wales McKinsey & Company (2016) The circular economy: Moving from theory to practice. McKinsey & Company. https://www. mckinsey.com/~/media/McKinsey/Business%20Functions/Sustainability%20and%20Resource%20Productivity/ Our%20Insights/The%20circular%20economy%20Moving%20from%20theory%20to%20practice/The%20circular%20economy%20Moving%20from%20theory%20to%20practice.ashx. Accessed 10 April 2018 McLellan BC, Williams RP, Lay J, van Riessen A, Corder GD (2011) Costs and carbon emissions for geopolymer pastes in comparison to ordinary portland cement. J Clean Prod 19:1080–1090. https ://doi.org/10.1016/j.jclep ro.2011.02.010 Miller D, Doh J-H, Mulvey M (2015) Concrete slab comparison and embodied energy optimisation for alternate design and construction techniques. Constr Build Mater 80:329–338. https ://doi.org/10.1016/j.conbu ildma t.2015.01.071 Nakamura S, Kondo Y (2002) Input–output analysis of waste management. J Ind Ecol. https ://doi.org/10.1162/10881 98023 20971 632 Nakamura S, Kondo Y (2006) A waste input–output life cycle cost analysis of the recycling of end-of-life electrical home appliances. Ecol Econ. https ://doi.org/10.1016/j.ecole con.2005.05.002 Nakamura S, Kondo Y (2009) Waste input–output analysis: concepts and application to industrial ecology. Springer, New York. https ://doi.org/10.1007/978-1-4020-9902-1 Nakamura S, Nansai K (2016) Input–output and hybrid LCA. In: Finkbeiner M (ed) Special types of life cycle assessment. Springer, Dordrecht, pp 219–291. https ://doi.org/10.1007/978-94-017-7610-3_6 Net Balance (2012) The Australian recycling sector. Department of Sustainability, Environment, Water, Population and Communities (DSEWPaC), Australia. http://www.envir onmen t.gov.au/syste m/files /resou rces/dc87f d71-6bcb4135-b916-71dd3 49fc0 b8/files /austr alian -recyc ling-secto r.pdf Accessed 3 June 2017 Norgate TE, Jahanshahi S, Rankin WJ (2007) Assessing the environmental impact of metal production processes. J Clean Prod 15:838–848. https ://doi.org/10.1016/j.jclep ro.2006.06.018 Olmez GM, Dilek FB, Karanfil T, Yetis U (2016) The environmental impacts of iron and steel industry: a life cycle assessment study. J Clean Prod 130:195–201. https ://doi.org/10.1016/j.jclep ro.2015.09.139 Orica (2016) Former chloralkali plant remediation project overview. http://www.orica .com/Locat ions/Asia-Pacifi c/Austr alia/Botan y/Botan y-Trans forma tion-Proje cts/Mercu ry-Remed iatio n/forme r-chlor alkal i-plant -remed iatio n-proje ct-overv iew#.WdRH8 1uCyM 8. Accessed 3 June 2016 Peters GP, Hertwich EG (2006) A comment on “functions, commodities and environmental impacts in an ecological–economic model”. Ecol Econ 59:1–6. https ://doi.org/10.1016/j.ecole con.2005.08.008 Productivity Commission (2006) Waste management, report no. 38. Australia Government Productivity Commission, Canberra, Australia. http://www.pc.gov.au/__data/asset s/pdf_file/0014/21614 /waste .pdf. Accessed 9 May 2017 Reynolds CJ, Piantadosi J, Boland J (2014) A waste supply-use analysis of Australian waste flows. Econ Struct 3:5. https :// doi.org/10.1186/s4000 8-014-0005-0 Säynäjoki A, Heinonen J, Junnonen J-M, Junnila S (2017) Input–output and process LCAs in the building sector: Are the results compatible with each other? Carbon Manag. https ://doi.org/10.1080/17583 004.2017.13092 00 Senaratne S, Lambrousis G, Mirza O, Tam VWY, Kang W-H (2017) Recycled concrete in structural applications for sustainable construction practices in Australia. Procedia Eng 180:751–758. https ://doi.org/10.1016/j.proen g.2017.04.235 Shan X, Zhou J, Chang VWC, Yang E-H (2017) Life cycle assessment of adoption of local recycled aggregates and green concrete in Singapore perspective. J Clean Prod 164:918–926. https ://doi.org/10.1016/j.jclep ro.2017.07.015 Stahel WR (2016) Circular economy: a new relationship with our goods and materials would save resources and energy and create local jobs. Nature 531:435–439 Suh S (2004) Functions, commodities and environmental impacts in an ecological–economic model. Ecol Econ 48:451–467 Suh S (2006) Reply: downstream cut-offs in integrated hybrid life-cycle assessment. Ecol Econ 59:7–12. https ://doi. org/10.1016/j.ecole con.2005.07.036 Suh S (2011) The structure of life-cycle environmental impact of the US economy. Paper submitted for the 2011 Leontief Prize by IIOA Suh S, Lenzen M, Treloar GJ, Hondo H, Horvath A, Huppes G, Jolliet O, Klann U, Krewitt W, Moriguchi Y, Munksgaard J, Norris G (2004) System boundary selection in life-cycle inventories using hybrid approaches. Environ Sci Technol 38:657–664. https ://doi.org/10.1021/es026 3745