Mixed-unit hybrid life cycle assessment applied to the recycling of 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 ash-based 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, 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.

3.1.1 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–A_p) 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 (f_p) 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.

The technology coefficient (A_IO) matrix is first calculated by dividing each transaction (x _ij ) in the T matrix by the total industry output (X _j ), and then deducted from the identity (I) matrix to yield the “I–A_IO matrix” for Eq. 3. The direct intensity multipliers (f_IO) vector expressed in kg of GHGE per $ (AUD) is obtained by dividing the IO environmental extension vector (E _j ) by the total industry output (X _j ).

3.1.3 Downstream cut-off matrix

The C^d 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 C^d 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 \(a_{hj}^{{{\bf{C}}^{\text{d}} }}\) (Eq. 1) and is expressed in kg/$, which represents the technology coefficient of RCA (Fig. 2). Subsequently, \(a_{ij}^{\bf{IO}}\) (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.

$$a_{hj}^{{\bf{C}^{\text{d}} }} = \frac{{x_{hj}^{{\bf{C}^{\text{d}} }} }}{{X_{j}^{\bf{IO}} }}$$

(1)

$$a_{ij}^{\bf{IO}} = \frac{{\left( {x_{ij}^{\bf{IO}} - x_{hj}^{{{\bf{C}}^{\text{d}} }} } \right)}}{{X_{j}^{\bf{IO}} }}$$

(2)

In this example, row h is the product RCA, row i is the product NA, and column j is the concrete industry sector: \(a_{hj}^{{{\bf{C}}^{\text{d}} }}\)  = technology coefficient matrix element (a) of row h and column j (in the C^d matrix; measured in kg/$); \(a_{ij}^{\bf{IO}}\)  =modified sales coefficient matrix element (a) of row i and column j (in the IO system; measured in $/$), in a case where a virgin material (from the IO system) is completely replaced by a recycled material (from the process system); \(x_{hj}^{{{\bf{C}}^{\text{d}} }}\)  = physical amount of product h used by sector j (in the C^d matrix; measured in kg); \(x_{ij}^{\bf{{IO}}}\)  = monetary amount of product i purchased by sector j (in the IO system; measured in $); \(X_{j}^{\bf{IO}}\) = total industry output of sector j (measured in $).

The concrete material flow data for C^d matrix is shown in Figs. 3 and 4 using STAN (Brunner and Rechberger 2004; Cencic and Rechberger 2008). Steel material flow data is based on Energetics (2012, Fig. 5). An example of calculating C^d with a matrix of eight processes, three industries, and five product sectors is available in the form of an Excel sheet and MATLAB script in supplementary information (SI) provided in Teh and Wiedmann (2017).

3.1.4 Upstream cut-off matrix

The C^u matrix complements the process system by adding any absent higher upstream monetary input data from IO flows to the process system (Suh 2004). The full procedure to construct the C^u matrix is employed from Wiedmann et al. (2011, Supplementary Information) to completely fill the C^u matrix in this study.

For this study, selected processes in the C^u matrix are replaced with IO sales coefficients (A_IO) of relevant Australian industry sectors from the use table of the IO system, which are then adjusted via multiplication with the Australian unit price of a functional unit of the process inventory employed. This represents the total cost of input process required to produce 1 kg functional unit of a process. To avoid double counting, all upstream inputs that are already accounted for in the process inventory employed are removed from the column vector. The unit price (AUD/kg) of FA, GGBFS, NaOH, and Na₂SiO₃ from Teh et al. (2017, Supplementary Information, Table A.3) are applied in this study. The prices of RCA and iron and steel scrap are obtained from Tam (2008) and ABARES (2010).

If technology coefficients data are absent in the IO system for a particular product, price-weighted coefficients need to be assembled from monetary data. For RCA, the Australian concrete recycling cost data is sourced from Tam (2008, Table 2), which is then assigned to a corresponding IO product sector. The technology coefficients vector is calculated by dividing each recycling cost in proportion to the total annual cost of concrete recycling. The price-weighted coefficient is then computed by multiplying the cost of 1 kg of RCA (0.02 AUD/kg from Tam (2008)) with the technology coefficients vector. Lastly, inputs that are already accounted for in the “Recycling brick rubble and concrete, at plant/AU U” AusLCI process inventory (Grant 2015) are deleted to avoid double counting. Selected processes for this study in the C^u matrix are represented as red columns in Fig. 5 and described in Additional file 1.

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 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 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 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.