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Towards a more circular construction sector: Estimating and spatialising current and future non-structural material replacement flows to maintain urban building stocks

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Humans are extracting and consuming unprecedented quantities of materials from the earth’s crust. The construction sector and the built environment are major drivers of this consumption which is concentrated in cities. This paper proposes a framework to quantify, spatialise and estimate future material replacement flows to maintain urban building stocks. It uses a dynamic, stock-driven, and bottom-up model applied to the City of Melbourne, Australia to evaluate the status of its current material stock as well as estimated replacements of non-structural materials from 2018 to 2030. The model offers a high level of detail and characterises individual materials within construction assemblies for each of the 13 075 buildings modelled. Results show that plasterboard (7 175 t), carpet (7 116 t), timber (6 097 t) and ceramics (3 500 t) have the highest average annual replacement rate over the studied time period. Overall, replacing non-structural materials resulted in a significant flow of 26 kt/annum, 36 kg/(capita·annum) or 721 t/(km2·annum). These figures were found to be compatible with official waste statistics. Results include maps depicting which material quantities are estimated to be replaced in each building, as well as an age pyramid of materials, representing the accumulation of materials in the stock, according to their service lives. The proposed model can inform decision-making for a more circular construction sector.
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This is a postprint version of the published journal article, available at: 1 https://doi.org/10.1016/j.resconrec.2017.09.022 2
Towards a more circular construction sector: estimating and 3
spatialising current and future non-structural material 4
replacement flows to maintain urban building stocks 5
André Stephana,1, Aristide Athanassiadisb,c
6
aFaculty of Architecture, Building and Planning, The University of Melbourne, Victoria 3010, Australia 7
bBuilding, Architecture and Town Planning, Université Libre de Bruxelles (ULB), 50 Av.F.-D. 8 Roosevelt, Brussels 1050, Belgium 9
cParis 1 University, UMR CNRS 8504 - Géographie-Cités - CRIA, 13 rue du Four, 75006 Paris, 10 France 11
12
1Corresponding Author 13
ORCID: 0000-0001-9538-3830 14
Tel: +61383445929 15
e-mail: andre.stephan@uclouvain.be (since Jan 2020)16
Abstract 17
Humans are extracting and consuming unprecedented quantities of materials from the earth’s crust. 18 The construction sector and the built environment are major drivers of this consumption which is 19 concentrated in cities. 20
This paper proposes a framework to quantify, spatialise and estimate future material replacement flows 21 to maintain urban building stocks. It uses a dynamic, stock-driven, and bottom-up model applied to the 22 City of Melbourne, Australia to evaluate the status of its current material stock as well as estimated 23 replacements of non-structural materials from 2018 to 2030. The model offers a high level of detail and 24 characterises individual materials within construction assemblies for each of the 13 075 buildings 25 modelled. 26
Results show that plasterboard (7 175 t), carpet (7 116 t), timber (6 097 t) and ceramics (3 500 t) have 27 the highest average annual replacement rate over the studied time period. Overall, replacing non-28 structural materials resulted in a significant flow at 26 kt/annum, 36 kg/(capita·annum) and 721 29 t/(km²·annum). These figures were found to be compatible with official waste statistics. Results include 30 maps depicting which material quantities are estimated to be replaced in each building, as well as an 31 age pyramid of materials, representing the accumulation of materials in the stock, according to their 32 service lives. The proposed model can inform decision-making for a more circular construction sector. 33
Keywords 34
Material flow analysis; maintenance; Melbourne; urban mining; life cycle assessment; urban 35 metabolism 36
1. Introduction37
Over the last century, the global population and material consumption increased by a factor of ~4 and 38 ~10, respectively (Krausmann et al., 2009). According to the same study, the use of construction 39 minerals increased by a factor of 42. This dramatic increase in annual material consumption per capita 40 has resulted in the accumulation of 792 Gt of materials in in-use stocks of buildings, buildings, 41 infrastructure and other manufactured goods in 2010. This represents a stock accumulation 23 times 42
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higher than at the start of the twentieth century (Krausmann et al., 2017). Krausmann et al. (2017) also 43 indicate that growth in material use and accumulation is unevenly distributed across the world, with 44 stock growth in China accelerating exponentially over the last decades. For instance, cement production 45 in China alone accounted for 55% of global production for the year 2010. From 2011 to 2013, China 46 produced as much cement as the United States over the twentieth century (Smil, 2013). 47
The unprecedented material consumption experienced after 1945 can be associated with the creation 48 and expansion of cities across the world as well as the rapid increase of global urban population. The 49 latter is projected to further increase by ~3 billion people by 2050 (United Nations - Department of 50 Economic and Social Affairs (UN), 2014), most of which in developing economies. This is expected to 51 lead to the creation of new urban areas, and associated additional material consumption. 52
When aligning these figures, the material requirements of modern societies, spearheaded by cities and 53 urban centres, become a self-evident societal, environmental and economic concern (Matthews et al., 54 2000). In fact, current volumes and trends of global material consumption and energy use drive local 55 and global environmental impacts, including resource depletion, climate change, and waste, among 56 others (Prior et al., 2012; Seto et al., 2014). In addition, the current linear economic model further 57 intensifies anthropogenic stress on natural resources, namely because of a very high demand for raw 58 material extraction on one side, and a significant dumping of pollutants and discarded materials on the 59 other, beyond the assimilative capacity of ecosystems. 60
The construction sector and the built environment consume the largest share of materials, globally 61 (Schandl et al., 2016), and represent the highest share of local waste production (Athanassiadis et al., 62 2016). The significance of the construction sector in terms of material consumption is expected to further 63 increase in the future (Fishman et al., 2016).The transition towards a more circular economy where 64 output flows could be reintegrated as secondary resources is being presented as a promising solution 65 at the construction sector (ABN-AMRO & Circle Economy, 2014; World Economic Forum, 2016), city 66 (City of Amsterdam, 2014; Institut d'Aménagement et d'Urbanisme de l'Ile-de-France, 2013; London 67 Waste & Recycling Board, 2015), national (Geng et al., 2012) and global level (Ellen MacArthur 68 Foundation, 2015). Nevertheless, current figures estimate the global economy is only about 6% circular 69 (Haas et al., 2015). Hence, a mismatch between policies, political aspirations and current practices 70 exists. In most cases, the majority of construction materials are only crushed and reused as aggregates 71 for roads (M. Hu et al., 2010). Such end-of-pipe solutions significantly downgrade the technical and 72 economic value of construction materials, addressing only partially the demand for natural resources 73 and waste generation and management (Cullen, 2017). 74
To realistically implement circular economy strategies for the built environment, such as urban mining 75 (Krook & Baas, 2013), it is crucial to have a better understanding of the type of materials that enter, exit 76 and are being stocked within cities. A number of studies have already assessed these flows and stocks 77 using a mix of bottom-up, top-down, static and dynamic approaches, as well as focusing on different 78 types of materials and spatial scales (inter alia Augiseau & Barles; D. Hu et al., 2010; Kleemann et al., 79 2016; Kral et al., 2014; Pauliuk et al., 2013; Tanikawa et al., 2015; Van Beers & Graedel, 2003; 80 Wiedenhofer et al., 2015a). These studies successfully provide information at the targeted scale, for 81 instance, material quantities available at a city scale. This information can identify pathways towards a 82 more circular economy. 83
Yet not enough studies provide spatialised results by building, allowing industry and other urban 84 stakeholders to localise secondary resources that become available at a certain time and estimate their 85 potential market (Kleemann et al., 2016; Tanikawa et al., 2015; Tanikawa & Hashimoto, 2009). While 86 an increasing number of authors are looking into stock spatialisation (see inter alia, Reyna and Chester 87 (2015) and Mastrucci et al. (2017)), there is still a need for ‘improved knowledge about stock-flow 88
dynamics […], and the spatial patterns of stock distribution’ (Krausmann et al., 2017, p. 1885). One of 89 the remaining hurdles to implement circular strategies and urban mining at a local and global level is to 90 provide more qualitative information on material flows exiting the built environment in order to 91 understand whether it is possible to reuse these materials as entering flows (Di Maria et al., 2013; 92
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Graedel et al., 2011). For instance, it is necessary to better understand from which building are 93 estimated material flows originating, when are these flows occurring, and whether it is possible to tap 94 into them. There is therefore a need for models that enable spatialising and estimating the occurrence 95 of material flows for building stocks at a high level of detail. 96
1.1. Aim and scope 97
The aim of this paper is to quantify and map annual non-structural material inflows and outflows 98 associated with the replacement of construction materials to maintain in urban building stocks in order 99 to support decision-making for a more circular built environment and construction sector. The City of 100 Melbourne, Australia is used as a case study. 101
This paper covers solely the replacement of non-structural materials over time, their magnitude, and 102 location. This is included in module B4 (material replacement) of the life cycle stage of a building, in the 103 European Standard 15978 (2011) on the environmental performance of buildings. Material flows 104 resulting from new construction or demolition are not taken into account. This is because material 105 replacement flows during the use life cycle stage of a building are usually understudied in urban stock 106 and flow models, notably in a detailed bottom-up manner such as in this study. The end-of-life stage of 107 these materials and their recyclability potential are also discussed. The scope of the paper is depicted 108 in Figure 1. 109
The bottom-up approach used in this paper has been originally developed for individual buildings by 110 Stephan (2013) and adapted to cities by Stephan and Athanassiadis (2017). Readers are referred to 111 the latter study for information regarding the modelling approach for buildings, bill of material quantities 112 (material inventory) estimations, embodied environmental requirements of the built environment and 113 other aspects. 114
1.2. Structure 115
Section 2 describes the proposed bottom-up approach including data requirements, quantification 116 algorithms, mapping tools, and its application to the City of Melbourne, Australia in order to illustrate its 117 potential. Uncertainty is also discussed in Section 2.5. Section 3 presents the results and Section 4 118 discusses the modelling approach and presents its limitations and future research steps, before 119 concluding in Section 5. 120
2. A dynamic and stock-driven bottom-up approach to estimate and spatialise future 121 construction material replacement flows in cities 122
This section describes the method proposed to estimate and spatialise future construction material 123 flows that enter and exit urban systems in order to maintain their building stock. The overall modelling 124 approach is presented, followed by data requirements, algorithms used, the case study description and 125 uncertainty in the model. 126
2.1. Overall modelling approach 127
The proposed stocks and flows model is a retrospective and prospective dynamic stock-driven bottom-128 up model (Augiseau & Barles, 2017; Muller et al., 2014). While its dynamic nature is similar to the model 129 proposed by B. Müller (2006), it is built up from individual materials into construction assemblies and 130 then buildings. A construction assembly is a group of construction materials or elements that serve a 131 particular function, e.g. an outer wall assembly providing weatherproofing and intimacy can be a brick 132 veneer wall, made of bricks, mortar, an air gap, a waterproof layer, insulation, timber-framed wall, water 133 vapour barrier, plasterboard and paint. 134
The model quantifies the bill of material quantities (material inventory) of every single building in a city, 135 based on its geometry and constituting assemblies. The geometry of a building is typically taken or 136 derived from the land-use database of a city or cadastral records. The specific assemblies used in a 137 building are harder to estimate, e.g. what type of outer walls, roof, internal walls, windows, etc. They 138 are typically assumed to be the same for every type of building in a city. This study uses the same 139
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archetypal approach (using 48 archetypes) as in Stephan and Athanassiadis (2017) and defines an 140 archetype as a unique combination of land-use, building age and height, with associated building 141 assemblies, specific to each archetype. For additional information, please refer to the open-access 142 detailed lists of building archetypes (Stephan & Athanassiadis, 2016b) and assemblies (Stephan & 143 Athanassiadis, 2016a) which are available on Figshare. Once the bill of material quantities (material 144 inventory) of a building is generated, the building material stock of the city is calculated as the sum of 145 all quantities in all buildings present in that city. 146
Input and output flows of construction materials are derived from material replacement rates (see 147 Section 2.2). They are equal in this study since the model assumes that as soon as a material/assembly 148 reaches its estimated service life, it is replaced by the same material/assembly. This assumes no 149 technological change over the coming 12 years (2018-2030), which is judged as acceptable as the 150 construction industry has been relying on similar materials and construction assemblies for years, 151 notably in terms of internal partitions, paint, carpet, timber, steel, and other materials. The only 152 exception would be materials in envelope assemblies, such as glass (for double glazing) and insulation. 153 However, these two combined represent 3.7% of the estimated material replacement flow (see Table 154 2). Therefore, this fixed technological framework is not expected to affect the results significantly over 155 the relatively short period of analysis. The replacement of materials at the end of their service life creates 156 both an output flow of construction materials that have been worn off and an input flow of new 157 construction materials. The following assumptions thus underpin the model used in this study: 158
A perfect 1:1 replacement of materials at their end of life; 159
No technological changes over the period of analysis; 160 No modifications due to socio-economic factors over the period of analysis; 161
No changes in land-use (or building archetype) over the period of analysis (e.g. conversion of 162 a warehouse into apartments); 163
No hibernating stocks (no materials are kept in buildings beyond their service life, although they 164 do not serve any functional purpose anymore (Wallsten et al., 2015)); and 165
No material leaching (i.e. materials such as paint and corrosive metals do not decay across 166 their service life). 167
The resulting material flows are linked to the geographical information system (GIS) for buildings and 168 can therefore be spatialised, over time. For each year in the period of analysis, the model is able to 169 provide a replacement flow of a given material, the buildings and assemblies where this flow is 170 occurring, and its magnitude. Therefore, the model is able to answer the questions: What? Where? 171 When? And How much? A capacity of spatial material stock models that is advocated for by Tanikawa 172 and Hashimoto (2009). The overall modelling approach is depicted in Figure 1. More details on 173 quantification steps and algorithms are provided in the next section. 174
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175
Figure 1: Overall modelling approach and scope. 176
2.2. Characterising construction material flows 177
As described in Stephan (2013), Stephan and Crawford (2014), and Stephan and Athanassiadis (2017), 178 the model used describes each material (e.g. glass in a glazing pane) as part of a construction assembly 179 (e.g. window). This allows the model to differentiate the service life of materials depending on their 180 function and the assembly they belong to. For example, timber used in doors will be replaced, while 181 structural timber will not (see Table 1). This approach is similar to the nested model that Busch et al. 182 (2014) use but with a static technological model. This is because the construction industry is one of the 183 least innovative and a minimal change in assembly composition is expected over the period of analysis 184 (2018-2030, see Section 2.4). 185
In this study, the model relies on average material service lives compiled from Ding (2004), NAHB and 186 Bank of America (2007) and Rauf and Crawford (2015), most of which have been used in previous life 187
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cycle assessment studies of buildings in Australia (e.g. Crawford et al. (2010) and Stephan et al. 188 (2013)). Table 1 presents the average service lives of construction materials and assemblies that are 189 used, along with other properties. Note that materials which are not replaced (e.g. Concrete), intervene 190 in the determination of the stock, and their properties are listed for transparency. A wastage coefficient 191 represents the percentage of material wasted during transportation from factory and construction on-192 site or off-site. For example, fibreglass insulation battens have a wastage coefficient of 1.1 meaning 193 that there is a 10% additional material requirement due to wastage during transportation and installation 194 (e.g. off-cuts). 195
Table 1: Properties of main construction materials used in the model. 196
Used in
Material
Type
Unit
Weight
(kg/unit)
Service life
(years)
Envelope
Structure
Systems
Finishes
Aluminium
Reflective foil
0.31
30
×
×
Aluminium
Roof coating
0.15
5
×
×
Aluminium
Interior shutters
0.34
12
×
×
Aluminium
Gutter
m
1.08
20
×
Aluminium
Frame
m
1.66
40
×
×
Aluminium
Door handle
no.
0.229
30
×
Aluminium
Exterior shutters
0.34
40
×
×
Aluminium
Virgin
t
1000
35
×
×
×
Aluminium
Sill
t
1000
40
×
×
×
Bitumen
Plain
1020
20
×
×
×
Carpet
Wool
2.5
10
×
Carpet
Nylon
2.5
10
×
Ceramics
Clay bricks (110 mm)
158
Not replaced
×
×
×
×
Ceramics
Tiles
23.2
50
×
×
Ceramics
Terracotta roof tiles (20 mm)
38.44
50
×
Ceramics
Fibre cement sheet (4.5 mm)
6.12
30
×
×
Ceramics
Fibre cement sheet (6 mm)
8.16
30
×
×
Ceramics
Toilet suite
no.
60
40
×
Ceramics
Basin
no.
14
35
×
Concrete
15 MPa
2400
Not replaced
×
×
×
Concrete
20 MPa
2400
Not replaced
×
×
×
Concrete
25 MPa
2400
Not replaced
×
×
×
Concrete
32 MPa
2400
Not replaced
×
×
×
Concrete
Aerated block (200 mm)
181.5
Not replaced
×
×
Concrete
Cement (structural)
t
1000
Not replaced
×
Concrete
Cement (other)
t
1000
25
×
Concrete
Hollow block (200 mm)
148.5
Not replaced
×
×
×
Concrete
Roof tile (20 mm)
38.44
50
×
Concrete
Mortar
1600
Not replaced
×
×
×
Concrete
Precast
2400
Not replaced
×
×
×
Concrete
Hollow block (100 mm)
74.25
Not replaced
×
×
Concrete
Hollow block (180 mm)
133.65
Not replaced
×
×
×
Concrete
25 MPa (low waste)
2400
Not replaced
×
×
×
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Used in
Material
Type
Unit
Weight
(kg/unit)
Service life
(years)
Envelope
Structure
Systems
Finishes
Copper
Pipe
m
0.082
30
×
Copper
Wire
t
1000
30
×
Glass
Clear float (4 mm) window pane
10
40
×
×
Glass
Toughened glass (6 mm)
16.2
40
×
×
Glass
Toughened glass (12 mm)
32.4
40
×
×
Insulation
Expanded polystyrene
24
50
×
×
Insulation
Fibreglass
12
30
×
×
Insulation
EPS (sandwich panel fill)
24
50
×
×
×
Paint
oil-based
0.069
10
×
×
×
Paint
water-based
0.077
10
×
×
×
Plasterboard
(10 mm)
12
30
×
×
×
Plasterboard
(13 mm)
15.6
30
×
×
×
Plastics
General (PVC)
t
1000
30
×
×
×
×
Plastics
Laminate (1 mm)
0.8
10
×
Plastics
Plastic membrane (1mm)
0.8
100
×
×
Plastics
Polystyrene (structural)
240
100
×
Plastics
PVC water pipe (20 mm)
m
0.05
25
×
×
Plastics
UPVC pipe (100 mm)
m
1.325
25
×
×
Plastics
UPVC pipe (100 mm slotted)
m
1.325
25
×
×
Plastics
Vinyl flooring (2 mm)
1.6
50
×
Plastics
Bath (Acrylic)
no.
20
40
×
Plastics
Wire coating
t
1000
30
×
Sand and stone
Sand
1600
Not replaced
×
×
×
Sand and stone
Screenings
2400
Not replaced
×
×
Steel
COLORBOND (R) steel decking
4
30
×
×
Steel
Reinforcement
t
1000
Not replaced
×
×
×
Steel
Lintel
t
1000
Not replaced
×
×
×
Steel
Stainless
t
1000
80
×
×
Steel
Steel decking
10
30
×
Steel
Gutter
t
1000
20
×
×
Steel
Door accessories
t
1000
30
×
×
×
Steel
Stainless (sink)
no.
7.25
40
×
Steel
Structural
t
1000
Not replaced
×
×
×
Steel
Reinforcement (prefabricated)
t
1000
Not replaced
×
×
×
Timber
Hardwood (structural)
800
Not replaced
×
×
×
Timber
Hardwood (exterior)
800
30
×
Timber
Softwood (framing)
800
Not replaced
×
×
×
Timber
MDF/particleboard
750
30
×
×
×
Timber
Softwood (panels)
800
30
×
×
Timber
Squirting (20 x 1.8 cm)
m
2.7
50
×
×
Timber
Hardwood ( window frame)
800
40
×
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Used in
Material
Type
Unit
Weight
(kg/unit)
Service life
(years)
Envelope
Structure
Systems
Finishes
Timber
Hardwood (poles)
800
50
Note: Service lives based on Ding (2004), NAHB and Bank of America (2007) and Rauf and Crawford 197 (2015); wastage coefficients are sourced from Wainwright and Wood (1981) and CSIRO (1994); and 198 weight intensities are based on each product. 199
For a given year y, the construction material replacement flow can be calculated as per Equation (1). 200 This is achieved by multiplying the quantity of a given material in each assembly of each building by a 201 wastage coefficient and by 1 or 0 depending the need for material replacement. The part of the equation 202 to the right of the bracket calculates if the time lapsed since the construction of the building is a multiple 203 of the service life of a particular material m. If it is, the fraction is an integer and the material is replaced 204 (value of 1) if not, the material is not replaced (value of 0). For example, given a 2030 time horizon and 205 a building constructed in 2012, carpet, with a service life of ten years will be replaced in 2022 ((y-206 CYb)/SLm, a, b = 1 | δ = 1) only since δ = 0 for all other years. The δ terms acts like a modified Dirac delta 207 survival curve distribution, which is often used in dynamic stock-driven models, alongside Weibull 208 distributions (Muller et al., 2014). The sum of all material replacements across the city is the material 209 replacement flow for a given year y. 210
 
 

 
 
 
 ,,
, , , , ,
11
,,
1
0
b
BA m a b
m y m a b m a b b
ba b
m a b
y CY
SL
CRF Q w CY y TH
y CY
SL
(1) 211
Where: 212
m = a given material (e.g. timber); y = a given year (e.g. 2020); b = a building; a = an assembly within 213 a building (e.g. windows); CRFm,y = city replacement flow of material m for year y, in functional unit of 214 material; Qm,a,b = design quantity of material m in assembly a of building b, in functional unit of material 215 (e.g. m³ of timber); wm,a,b = construction waste coefficient of material m in assembly a of building b (e.g. 216 1.05); δ = a modified Dirac delta function; CYb = construction year of building b (e.g. 1987); SLm,a,b = 217 service life of material m in assembly a of building b (e.g. 30 years); + is the set of positive integers; 218 and TH = time horizon (e.g. 2030). 219
2.3. Data requirements 220
The bottom-up building stock model requires a detailed building geometry database, a land-use 221 database (used to derive assembly archetypes for buildings), a GIS database of the building stock for 222 spatialisation and, most importantly for material flow analysis, a detailed database of material service 223 lives alongside the year of construction of each building. A database of wastage coefficients for 224 construction materials can also be used to account for on-site construction waste (as in this study). 225 These data allow the assessor to quantify and spatialise material flows associated with material 226 replacement, across the city. Data requirements and potential extensions are summarised in Figure 1. 227
2.4. Application to the City of Melbourne, Australia 228
The City of Melbourne is used as a case study to illustrate the potential of the developed model. The 229 City of Melbourne is chosen for the availability of detailed open data and its heterogeneous building 230 stock of ~14 000 buildings. All details pertaining to the application of the model to the City of Melbourne 231
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are provided in Stephan and Athanassiadis (2017). Only a brief description of the case study city is 232 given below. 233
234
Figure 2: Location and map of the City of Melbourne with buildings differentiated by typology. Note: The 235 City of Melbourne map and the number of buildings is based on data from City of Melbourne (2015a); 236 population figures are as of 2015 and are based on City of Melbourne (2015b). 237
238
The City of Melbourne covers most of the inner core of Metropolitan Melbourne (or Greater Melbourne), 239 Australia, the second most populous city after Sydney with a population of ~4.7 million inhabitants living 240 in the metropolitan area according to the latest census (ABS, 2017). The City of Melbourne has a broad 241 range of publically available datasets (City of Melbourne, 2017), notably the Census of Land Use and 242 Employment (CLUE) database (City of Melbourne, 2015a) which contains the floor area by land-use, 243 the year of construction, and the number of stories for most of the 14 385 buildings. The land-use types 244 are diverse within the City of Melbourne, as depicted in Figure 2. This makes the application of a 245 material flow analysis model more interesting as construction materials used in particular buildings (e.g. 246 hospitals) are taken into account. Out of the 14 385 buildings 13 075 were retained in the model and 247 the rest were excluded because they either had a gross floor area of less than 45 or they were 248 exceptional buildings, such as sports complexes (e.g. the Melbourne Cricket Ground) or train stations 249 (e.g. Southern Cross Train Station). 250
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2.5. Uncertainty 251
While there are many sources of uncertainty affecting the model, two main categories can be identified 252 and are expected to affect the outputs most, namely parameter uncertainty and model uncertainty. 253 These are described in a more theoretical sense in a range of studies, such as Huijbregts (1998). 254
Parameter uncertainty is mostly present in the service lives of materials which are paramount for the 255 reliability of the proposed model. Realistically estimating these service lives is extremely difficult for a 256 given building as they depends on a broad range of factors, including the physical properties of a 257 material, climate, construction quality and detailing, user taste and behaviour and others (Aktas & Bilec, 258 2012; Thomsen & van der Flier, 2011). The International Standard 15686-1 (2011) defines a set of 259 seven different factors that can influence the service life of a construction material. The base service 260 life of a material, based on empirical data, is multiplied by all seven factors to obtain a corrected service 261 life, adapted to the particular material in a specific building. However, the choice of these factors can 262 be very subjective as discussed in Hovde and Moser (2004). Therefore, the so-called ‘factor method’ is 263 not considered in the model as it would further increase uncertainty and only the base service life is 264 used. The same base material service lives are used across the stock but are expected to vary 265 significantly between buildings in reality. This means that at a building scale, the reliability of material 266 replacement flows for a given year is low. The level of confidence increases when quantifying the 267 average or total material flow over a number of years. At a city scale (e.g. for the City of Melbourne and 268 its 13075 modelled buildings), results are much more reliable in general as this variability is significantly 269 reduced following the law of large numbers (with N= 13 075 buildings). 270
Another source of uncertainty is assumptions in the model itself, notably the 1:1 replacement of 271 materials. The proposed model assumes that the input flow balances the output flow as materials are 272 replaced, but this is not always realistic as construction assemblies could be replaced with other 273 assemblies with different material compositions. For example, an aluminium-framed single-glazed 274 window from the early 1980s could be replaced by a timber-framed double-glazed window today. It is 275 however hard (if not impossible) to reliably forecast this in a model. Instead, what-if scenarios (Pesonen 276 et al., 2000) can be run to evaluate the influence of the uptake of certain construction materials or 277 assemblies on the material flow in the city. The model does not currently account for these potential 278 future scenarios and can be a source of uncertainty. However, it can be further extend to allow for 279 differentiated material replacements (see Section 4.4). 280
To summarise, this model, like any other, suffers from uncertainty, notably from parameter and model 281 uncertainty. Results are not expected to be very reliable for a single building in the city, and that is 282 mostly due to variability in material service lives as well as modelling uncertainty. At the whole city level, 283 results are expected to be reliable due a significant drop in parameters variability (law of large numbers) 284 but would still suffer from the model uncertainties associated with the chosen assumptions (see Section 285 2.1). 286
3. Results 287
This section presents some of the results that can be obtained by applying the developed model to the 288 City of Melbourne. Figure 3 presents the age pyramid of main construction materials present in the City 289 of Melbourne’s building material stock in 2015. Borrowing this graphical representation from 290 demography can facilitate a better understanding of the state of the stock. It also offers a visual tool to 291 estimate major replacement flows. The use of age pyramids to represent material stocks is discussed 292 by Cabrera Serrenho and Allwood (2016) in their study of material stock demographics of cars in Great 293 Britain. 294
The pyramid shows the accumulation of hard-wearing materials in the building stock, e.g. concrete, 295 steel, timber, and how non-durable materials typically do not remain in the stock for a long time, e.g. 296 carpet and plasterboard. It allows stock managers (construction and demolition companies) to 297 anticipate periods of major replacements, by comparing the stock of a material with its typical service 298 life (see carpet and plasterboard as examples). For instance, there is a significant amount of 25-years-299
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old plasterboard (see bar A in Figure 3) that would need to be replaced in 5 years (assuming a typical 300 service life), resulting in a large material replacement flow that should be managed in a manner to create 301 most opportunities, for the environment, society and the economy. 302
The pyramid also shows years of significant construction activity. For example, in 1960 (year 55 in the 303 pyramid), a large amount of construction materials was added to the stock due to the reported 304 construction of three medical facilities in the CLUE database. Similarly, the spikes occurring for 1990-305 1992 (years 23-25 in the pyramid), are related to the construction of large office and apartment buildings 306 in the CBD. The top 15 heaviest buildings (out of 317 constructed during that period), represent alone, 307 67% of the net addition to stock during those years. Large individual building projects can therefore 308 have a notable effect on the material stock profile of a city council. 309
Using a pyramid to represent the stock also allows an easy comparison of the order of magnitude of 310 material quantities. For instance, the mass of concrete alone is, on average, thirteen times more than 311 those of all other materials combined. Overall, the mass of the estimated material stock in buildings of 312 the City of Melbourne is 56,007 kt as of 2015. This is broken down into concrete (51,412 kt | 91.8%), 313 steel (1,983 kt | 3.5%), ceramics (944 kt | 1.7%), timber (378 kt | 0.7%), plasterboard (243 kt | 0.4%), 314 carpet (71 kt | 0.1%), glass (58 kt | 0.1%) and other materials (918 kt | 1.7%). For a breakdown of the 315 2015 material stock by construction assembly, please refer to Stephan and Athanassiadis (2017). 316
It is important to note that the level of uncertainty associated with the construction year of buildings and 317 the assumed service life is compounded and increases as we move upward in the pyramid. Years close 318 to its base contain the most reliable data. This means that stock estimates for recently constructed 319 buildings and for easy-wearing materials is more accurate than for old buildings and hard-wearing 320 materials. 321
Table 2 contains the annual estimated material flows for the City of Melbourne, by material, from 2018 322 to 2030 (time horizon). The largest estiamted material replacement is expected to occur in 2020 with 323 73 kt (Gg) of materials being replaced, followed by 2030 with 50 kt (Gg). The increase for 2030 across 324 most materials is due to different drivers for different materials. For instance, for steel, glass and 325 aluminium, the top 15 buildings by replacement flow represent alone, 46%, 48% and 49% of the 2030 326 replacement flow, respectively. Paint and timber are less concentrated, with the top 15 buildings by 327 replacement flow representing 16% and 29% of the total. The percentage of buildings in which a 328 material replacement is estimated to occur during 2030 is as follows: Paint 30%, Ceramics 15%, 329 Plastics 15%, Aluminium 14%, Carpet 13%, Steel 13%, Timber 10%, Plasterboard 7%, Glass 6% and 330 Insulation 3%. This percentage is calculated as a fraction of the total building stock. If only buildings in 331 which the material is actually installed are considered, the share of buildings in which carpet is replaced 332 rises to the top for 2030, at 36%. 333
These numbers are estimates only and rely on typical material services lives (see Table 1). However, 334 on average, the City of Melbourne is expected to require 26 kt (Gg) of new materials per year (excluding 335 paint calculated at 0.8 kt), to maintain its 2015 building stock over the period 2018-2030. This will also 336 generate 26 kt (Gg) of construction material waste (not including new construction and demolitions). 337 This equates to 721 t/(km²·annum) or 0.5 kg per square metre of building gross floor area (excluding 338 underground parking) per annum. The annual flow per capita for the City of Melbourne, obtained by 339 dividing each year’s material replacement flow by the projected population (residents, workers and 340 students only based on City of Melbourne (2015a)), is 36 kg/(capita·annum). 341
While there are no publically available construction and demolition data for the City of Melbourne, state-342 side data can be used to provide some context around these material replacement flows. In 2010-2011, 343 the state of Victoria (including Greater Melbourne and all other cities and townships) generated 4 528 344 kt of construction and demolition waste (DSEWPaC and Blue Environment, 2014). From these, an 345 estimated 3 492 kt (78%) are associated with concrete (2 537 kt), bricks (622 kt) and rubble (362 kt), 346 which are not modelled in the material replacement flows addressed in this study. An additional 427 kt 347 are hazardous waste and other materials not covered, leaving 580 kt associated mainly with timber 348 (146 kt), metals (97 kt) and plastics (23 kt). Using a Victorian population of 5 509 798 inhabitants in 349
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2010-2011 (as in DSEWPaC and Blue Environment (2014)), this equates to 105 kg/capita for that year. 350 This figure is for non-structural construction materials (except steel) that are deemed similar to those 351 considered in this study, but would exclude some other modelled materials such as carpet. In 352 comparison, the estimated 36 kg/(capita·annum) of material replacement flow to maintain the building 353 stock of the City of Melbourne over 2018-2030, represents 34% of this figure. While a more thorough 354 validation is needed (which would require data that are not currently available), this provides some 355 confidence to the results, given that the 105 kg/capita figure also encompasses demolition activity. 356
When comparing the material replacement flow to the total construction and demolition waste statistics 357 (including all materials), it represents 4.4% of the annual construction and demolition waste per capita 358 across the state of Victoria, Australia, which is 822 kg/capita (DSEWPaC and Blue Environment, 2014). 359 Assuming a 1.5% renewal rate of the building stock of the City of Melbourne per annum, the material 360 replacement flow would represent 3% of the total (material replacement (26 kt) + demolition flow (840 361 kt)). This is also in line with the 4.4% figure above. 362
Figures 4a and 4b depict the spatialised accumulated construction material replacement flow, by 363 building, for the period 2018-2030. While results are calculated per year, an accumulated value over a 364 time period covers potential variability in the time of material replacement as the exact year of 365 replacement is almost impossible to predict reliably (see Section 2.5). For all materials except carpets, 366 a single replacement occurs between 2018 and 2030. Carpet is replaced twice in 17.4% of buildings 367 and once in the rest. Such maps can be a valuable tool for decision-makers, construction and demolition 368 companies, waste management companies, architects, urban planners and other actors of the built 369 environment that are interested in understanding where flows are likely to occur for a given year range 370 (and possibly for a single year, if more reliable data become available) and in what intensity. 371
372
373
374
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375
Figure 3: Estimated age pyramid of the City of Melbourne’s construction material stock in 2015, for main materials, with concrete on the left and other 376 materials on the right. Note: The mass of concrete is one order of magnitude higher than all other materials combined; original in colour. 377
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Table 2: Estimated input flows associated with the replacement of non-structural materials to maintain 378 the 2015 building stock of the City of Melbourne, by material, from 2018 to 2030, in tonnes (Mg). 379
Materi
al
Year
Steel (non-
structural)
Timber (non-
structural)
Glass
Aluminium
Ceramics
Insulation
Plasterboard
Plastics
Carpet
Total
2018
94
2 470
537
326
2 791
60
4 366
164
4 217
15 026
2019
90
2 327
246
151
1 860
62
4 466
129
5 556
14 888
2020
2 503
27 359
1 812
752
8 277
226
19 279
909
11 710
72 826
2021
126
4 839
648
412
1 669
69
10 126
166
8 962
27 017
2022
169
7 078
545
413
3 010
67
6 048
240
7 116
24 686
2023
79
2 738
764
490
3 242
51
4 347
180
7 025
18 915
2024
99
2 532
352
233
1 777
54
3 199
171
3 479
11 895
2025
193
4 616
963
289
3 804
78
6 147
248
5 251
21 589
2026
99
3 376
770
541
3 510
61
5 027
209
13 698
27 289
2027
137
4 980
474
323
2 558
66
7 866
218
4 010
20 631
2028
168
2 013
829
578
2 448
67
3 490
174
4 217
13 986
2029
141
3 989
590
436
2 765
63
6 515
192
5 556
20 248
2030
1 397
10 944
3 101
1 857
7 785
242
12 404
708
11 710
50 148
Profile
Annual
Average
407
6 097
895
523
3 500
90
7 175
285
7 116
26 088
Total
5 295
79 261
11 631
6 801
45 496
1 166
93 280
3 708
92 507
339 144
Fraction of
total stocka
0.3%
21%
20%
24%
5%
14%
38%
19%
130%
1%
Note: a represents the total replacement flow over 2018-230 as a fraction of the total material stock, 380 including structural materials. 381
Three main observations can be drawn from Figures 4a and 4b. These are discussed below. 382
Firstly, the accumulated flows (input and output) of plasterboard, timber and carpet are an order of 383 magnitude higher than those of the other materials presented. The maximum building-specific 384 replacement flows across the building stock, are, by material: timber (3 530 t), carpet (1,457 t), 385 plasterboard (1 168 t), ceramics (981 t), glass (223 t), steel (197 t), aluminium (148 t), plastics (79 t) 386 and insulation (24 t). Both the quantity of material used in a building and its density affect these figures. 387
Insulation, with a very low density and a limited quantity is logically the least significantly flow, both at 388 the individual building level and at the whole city level (see Table 2). Nevertheless, the flow of insulation 389 materials is important as some insulation materials can be hard to recycle. In the case of Melbourne, 390 the dominant majority of thermal insulation installed is fibreglass, with significant embodied energy 391 (Crawford & Treloar, 2010), and is therefore critical to re-use or recycle, although it is very hard to do 392 so. Recent studies (e.g. López et al. (2012), indicate that pyrolysis could be used to recycle fibreglass 393 into useful products. Fibreglass could also be re-used in cement production (Dehghan et al., 2017), or 394 recycled into new construction products, typically by separating the glass fibres and using them as 395 reinforcement, e.g. in polypropylene underfloor vents, railway sleepers (Job, 2013). There is therefore 396 a strong incentive to find solutions to increase the circularity of fibreglass, notably in the case of 397 Melbourne and Australia in general, where it is widely used in construction. 398
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Secondly, it is important to interpret these maps in light of Figures 5 and 6 from Stephan and 399 Athanassiadis (2017) which depict the total material stock as well as the stock of steel, glass and carpet 400 for each building. While the quantities of stock and replacement flows can be very different, the 401 comparison reveals what share of the material stock is replaced for each building, from 2018 to 2030. 402 Across the entire building stock, 0.3% of the steel stock (including structural steel), 20% of the glass 403 stock and 130% of the carpet stock is estimated to be replaced (see Table 2, bottom row). 404
Thirdly, it is important to note that some buildings do not have any replacement flow (represented in 405 light grey). This is because either no materials are replaced during the period of analysis (the building 406 is recent or the material service life is not reached during this time period) or because these materials 407 are not present in the building. 408
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409
Figure 4a: Estimated accumulated building material replacement flows in the City of Melbourne, for 410 plasterboard, timber, carpet and aluminium, from 2018 to 2030 411
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412
Figure 4b: Estimated accumulated building material replacement flows in the City of Melbourne, for 413 ceramics, glass, insulation and steel, from 2018 to 2030 414
4. Discussion 415
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This section discusses the model and its application to the City of Melbourne. It covers the contribution 416 of this study to the body of knowledge and situates the model within a broader context of actions 417 targeting a more circular economy for the construction sector. The limitations of the model and 418 associated future research directions are also discussed. 419
4.1. Contribution of the developed model 420
This study has shown that a dynamic, stock-driven, bottom-up modelling of urban building stock can 421 facilitate the management of material replacement flows through a detailed spatial and temporal 422 analysis. Such models allow decision-makers to identify major flows of materials, anticipate time periods 423 of intense material replacements or flows and better understand where these flows take place. The 424 model is applied to the City of Melbourne (see also Stephan and Athanassiadis (2017)) but can be used 425 in other cities, as long as necessary data are available (see Section 2.3). Two main observations can 426 be noted. 427
Firstly, material flows associated with the replacement of non-structural construction materials to 428 maintain in-use urban building stocks are not negligible. In the case of the City of Melbourne, they are 429 estimated at 26 kt/annum, 721 t/(km²·annum), 36 kg/(capita·annum) or 0.5 kg/m² (gross floor area; 430 excluding underground parking) on average, from 2018 to 2030. It is hard to compare these figures to 431 others in the literature as few studies use a similar approach to quantify material flows, and specifically 432 material replacement flows. Among these studies, Wiedenhofer et al. (2015b) quantify material flows 433 associated with the maintenance and replacement of the residential building stock in European Union 434 (63.385 million buildings) using a top-down approach. They find an average replacement flow of 3.21 435 t/annum per building from 2004 to 2009, compared to 1.31 t/annum for residential buildings in the City 436 of Melbourne (9 858 residential buildings), from 2018 to 2030. In the absence of any indication about 437 the total floor area modelled or the average dwelling size in Wiedenhofer et al. (2015b), this comparison 438 only allows us to observe that the two flows are of the same order of magnitude and that replacement 439 flows associated with maintenance cannot be neglected, notably for mature building stocks with a low 440 construction/demolition activity. 441
Secondly, the application of the model to the City of Melbourne reveals that individual buildings can 442 have a notable impact on the material replacement flow at a municipality level. The top 15 buildings in 443 terms of annual material replacement flow, represented on average 36.8% of the total annual flow for 444 the City of Melbourne with a minimum of 16% for paint, a maximum of 49% for aluminium, a standard 445 deviation of 9.6% and a median of 36.5%. The simultaneous renovation of very large buildings or 446 complexes in a city can therefore result in a spike in the material flow (see Table 2 and Figure 3) and 447 offer significant potential for material recovery, re-use and recycling. Spatialising these average annual 448 flows (see Figures 4a and 4b) unlocks potential synergies for material re-use between different sites 449 and also from buildings to other sectors of the economy (see Section 4.2). 450
Quantifying and spatialising the replacement flows of urban building stock provides information about 451 how much and where secondary resources are available in order to mitigate the use of new material 452 and waste generation. However, this only represents a single step towards a more circular economy of 453 the construction sector. Indeed, besides the recovery and recycling of replacement flows that exit the 454 building stock, there are a number of other measures that could be implemented to make building stocks 455 more circular. 456
4.2. Towards a circular economy for the construction sector 457
Transitioning to a more circular economy for the construction sector in the City of Melbourne requires 458 to first understand the current rate of material recovery. Using state-wide data as a proxy for the City of 459 Melbourne (due to the absence of statistics at this scale), 69% of construction and demolition waste 460 was recovered in 2010 based on DSEWPaC and Blue Environment (2014). Using the raw data on which 461 that source is compiled, this 69% recovery rate varies widely by material category, with a reported 0% 462 for glass, 14% for plastics (PET, HDPE, PVC, LDPE, PP and PS), 18% for timber, 79% for masonry 463 materials (Asphalt, Bricks, Concrete, Rubble and Plasterboard/Cement sheeting) and 93% for metals 464
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(Steel, Aluminium and other non-ferrous metals). Unfortunately, the breakdown of the materials sent to 465 landfill is not available and therefore specific recycling rates for a material within a material category, 466 e.g. plasterboard within masonry materials, is not available. Based on these statistics, there seems to 467 be significant scope for increased recovery of glass, plastics and timber; a moderate margin of 468 improvement for masonry materials; and a limited margin for metals, which are almost completely 469 recovered. There is no existing data for carpet but if data for plastics is used as a proxy, recovery rates 470 are low. This means that glass, plastics and timber would need to be better recovered and additional 471 processing factories would need to be set up to process them and re-inject them in the economy. The 472 estimated annual flows of 895, 285 and 6 097 t/annum for glass, plastics, timber, respectively, from 473 2018-2030 and for the City of Melbourne could drive pilot recovery projects. In addition, carpets could 474 also by recycled and are estimated at 7 116 t/annum. Recycling carpets still requires further research, 475 but progress has been made in developing processes to turn wool carpets into fertiliser (McNeil et al., 476 2007) and to recycle nylon carpets (Braun et al., 1999; Zhang et al., 1999). Such pilot projects for 477 increased material recovery and circularity would capitalise on material flows within the Greater 478 Melbourne Metropolitan area as well as on construction and demolition flows. 479
It is also important to consider the circularity of the City of Melbourne’s building stock within the broader 480 economy. The outflows of materials could be recirculated within the construction sector (e.g. concrete 481 downcycled as crushed aggregates for roads (Wiedenhofer et al., 2015b)), but also in other sectors. 482 Similarly, the construction sector can recirculate recovered materials from other sectors, such as textiles 483 as thermal insulation (Briga- et al., 2013). 484
Information from the model can facilitate the initiatives above, which also fit within the Metropolitan 485 Waste and Resource Recovery Implementation Plan (MWRRG, 2016) for Greater Melbourne. For 486 instance, the difference of annual replacement flows between materials (see Table 2) could encourage 487 city officials to devise specific circular strategies for these materials given that there is a sufficient critical 488 mass for companies to be involved and change their current practices. This critical mass is pivotal to 489 ensure both financial and environmental benefits and avoid a circular economy rebound effect (Zink & 490 Geyer, 2017). However, there are additional parameters that could influence both urban administrations 491 and companies to transition towards more circular practices, such as the price of materials and the ease 492 of disassembly of materials. Metals such as steel and aluminium are therefore of primary importance 493 for construction and demolition companies due to their economic value. They are also particularly 494 interesting from a circular economy perspective as they are more recyclable compared to other 495 materials (e.g. fibreglass) without significant loss of mechanical properties (although this option is 496 typically energy intensive). This is anyhow reflected in the very high rate of recycling of metal waste 497 (93%) (DSEWPaC and Blue Environment, 2014). In summary, the current public data on construction 498 material flows indicates that higher rates of recovery and circularity are achievable for certain material 499 categories through a combination of intra- and inter-sectorial circularity. 500
At a more international level, numerous strategies can be deployed to transition to a more circular 501 construction sector. For instance, more sustainable and durable materials, such as renewable materials 502 manufactured using renewable energy sources, can be encouraged. These would gradually replace 503 non-renewable and less durable materials where possible. The environmental benefits from using more 504 sustainable materials could be measured using the developed model, by using associated coefficients 505 of embodied energy, water, emissions and other requirements. 506
Another step towards a circular building sector is design for disassembly. As mentioned above, the 507 calculated replacement flows exiting the City of Melbourne are not necessarily ready to be reused 508 and/or recycled. Most of them are tangled with other materials hindering their separation, optimal reuse 509 in other buildings, or recycling. Thus, in order to optimise the reuse of flows it is necessary that future 510 buildings and building assemblies are designed to be better disassembled. Modular and prefabricated 511 construction can broaden the possibilities of reuse (ARUP, 2016). This calls for a close collaboration 512 between architects and manufacturers to conceptualise new construction details and connections 513 between building assemblies. At this stage, the developed model does not distinguish whether building 514 assemblies are designed specifically for disassembly, therefore the estimation of exiting flows does not 515
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necessarily represent how much could actually be reused. This information could be an additional 516 qualitative indicator for each of the building assemblies available in the model. 517
A crucial step that would need to be implemented before building stocks become more circular is a shift 518 of perception in the ownership of construction materials. In fact, in order to maximise reuse and 519 recycling rates, materials could be owned by manufacturing or real estate companies over their entire 520 period of use (which could span multiple service lives) to maintain their economic and technical values 521 as much as possible. Thus, construction materials would be provided as services instead of products 522 to building occupants (product service system). For instance, Philips (pay-per-lux)1 and Desso (take-523 back)2 have implemented such strategies, where only a service is sold to the client, such as square 524 metres lit or ‘floor covered by carpet. When the materials provided through the service do not cover 525 the demand of the client anymore, they are taken back, repaired and replaced. This shift in ownership 526 encourages manufacturing companies to develop recovery schemes, material and energy efficiency 527 strategies to refurbish as effortlessly as possible their products, as well as producing components that 528 have a longer service life. This shift would help enhance the circularity of the construction sector from 529 a producer’s perspective. 530
However, there are potential barriers in the way of product service systems, such as consumers being 531 under the impression that they are not in control (Tukker, 2015). Moreover, the management of the end-532 of-life stage of a material within a product service system would need to consider the amount of 533 embodied energy required to restore the material’s quality compared to producing it anew, as well as 534 the quantity of material recovered compared to the total material quantity required for primary production 535 (Cullen, 2017). Nevertheless, there is potential for improved environmental performance should these 536 challenges be overcome. While the model does not currently allow modelling the environmental and 537 economic benefits of a shift in procurement and ownership, this could constitute future research. 538
Finally, another actor that would impact the transition towards a circular economy is local governments 539 and administrations. For instance, all of the circular economy strategies above could be enforced 540 through policy-making from local (or national) governments. A more immediate approach could consist 541 of governments fixing a percentage of recovery rate from buildings to be demolished or renovated as 542 well as the use of secondary materials in new buildings or during renovation operations within public 543 procurement processes and public works (ABN-AMRO & Circle Economy, 2014). Different scenarios of 544 reuse could be modelled using the proposed model in order to evaluate their potential for reducing both 545 construction and demolition waste and the use of new construction materials (and associated embodied 546 environmental requirements). 547
The systemic and complex nature of a circular economy requires change both at a company level and 548 at the entire construction sector level. This implies that policy-making and communication between 549 companies could help as much as engineering and design innovations to achieve the transition towards 550 a circular economy in the construction sector and the built environment (ARUP, 2016). As established 551 in Pomponi and Moncaster (2017), transitioning to a circular built environment will require research 552 across economic, environmental, behavioural, societal, technological and governmental dimensions. 553
4.3. Limitations 554
The proposed model has limitations, like any other. It does not include furniture, heating, ventilation and 555 air conditioning (HVAC) and lighting equipment, electronic appliances, and other goods contained within 556 buildings. These flows can represent a non-negligible amount of material flow or even flows with a 557 higher economic value per tonne or recyclability rate due to their material composition (Wallsten et al., 558 2013; Yamasue et al., 2013). In addition, this model solely focuses on buildings, omitting transportation 559
1 http://www.ellenmacarthurfoundation.org/case-studies/selling-light-as-a-service
2 http://www.desso.fr/globalaccounts/regus/take-back%E2%84%A2-programme/
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infrastructure as well as water and energy grids which can be significant. For example, they represent 560 ~15% of the total urban material stock of the Brussels Capital Region, Belgium (Institut Bruxellois de 561 Gestion d'Environnement (IBGE), 2015). Also, the presented research does not address associated 562 embodied requirements which were already presented in a previous study by the authors (Stephan & 563 Athanassiadis, 2017). Furthermore, while this study uses 48 different archetypes and associated 564 assemblies, a higher resolution would further improve the quality of the data in the model. In addition, 565 the model uses a deterministic Dirac-delta function to replace materials based on defined service lives. 566 Instead, probabilistic survival curves, such as those used in Miatto et al. (2017) could be drawn from to 567 randomly replace a material (value of 1 in the right term of Equation 1) at its end of life. The resulting 568 stochastic model would be more realistic but to the authors’ knowledge, there is currently limited data 569 to model the actual survival curves for materials and assemblies. The model also suffers from 570 uncertainty, notably due to the lack of accurate data, the use of an archetypal approach and the difficulty 571 of reliably predicting material replacements (see Section 2.5 for more details). Finally, the model 572 focuses on material replacement flows and does not account for new construction and demolition flows 573 which can be drastically higher although less predictable from a spatial perspective. Despite these 574 limitations, the strength of the model lies in its adaptability and capacity for improvement. As such, these 575 limitations could constitute the basis of future research to improve the model. 576
4.4. Future research 577
The model discussed in this paper and its application to the City of Melbourne pave the way for myriad 578 future research. These include, expanding the scope of the model to account for new construction as 579 well as demolition activity, quantifying embodied environmental requirements associated with material 580 replacement flows and further detailing the model to investigate different replacement and end-of-life 581 scenarios. 582
By coupling these model with future scenarios, such as city development plans and anticipated 583 demolition activity, material flows resulting from construction and demolition can also be captured. 584 These flows are usually much more significant than material replacement flows, as shown in 585 Wiedenhofer et al. (2015b). Knowing that in many dense urban cores, industrial land with a low material 586 intensity per square metre is typically repurposed for high-rise or high-density development with 587 significantly higher material requirements, the material stock can be expected to increase significantly, 588 causing ‘spikes’ of material stocks, both spatially (e.g. visible on maps such as Figure 4) and temporally 589 (e.g. visible in the age pyramid, Figure 3). 590
Embodied environmental requirements associated with material replacements can also be quantified. 591 For example, using hybrid embodied energy coefficients from Crawford and Treloar (2010), the 592 embodied energy associated with material replacements from 2018 to 2030 across the City of 593 Melbourne (~50 million m² of buildings) represents ~50 000 TJ. This is equivalent to the initial embodied 594 energy of 3.3 million m² of new 200 m² suburban detached houses in Melbourne (based on data from 595 Stephan and Crawford (2016)). By favouring re-use and recycling, the amount of recovered embodied 596 energy, water, greenhouse gas emissions (or other environmental flow) can be estimated. This will 597 enable decision makers to better understand the implications of their construction and demolition waste 598 management strategies. 599
Aside from these two main areas of future research, the proposed model could allow the assessor to 600 impose the replacement of a certain assembly by another, within certain timeframes, e.g. systematically 601 replace single-glazed windows with double glazed windows from 2020 onwards. This would move 602 beyond the two main assumptions in this work, which are a 1:1 replacement ratio and no technological 603 change over the period of analysis. This feature would allow a more flexible analysis of future scenarios, 604 although the information available to model such changes is not always available or reliable. 605
Another significant area of future research revolves around participatory data gathering. Encoding the 606 actual assemblies used in a building would significantly improve the accuracy of the model, compared 607 to using an archetypal approach. A collaborative platform where building occupants can upload 608 assembly compositions and obtain an estimation of the quantity of materials in their building, associated 609
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embodied requirements and anticipated replacements would help collect more reliable data and reduce 610 parameter uncertainty (see Section 2.5). 611
Finally, from a data representation and decision-making perspective, the age pyramid (Figure 3) could 612 be coupled with information on material quality and the associated assembly in which each material is 613 used. For example, glass and ceramics can be disaggregated and glass in single and double-glazed 614 windows can be further differentiated. This could provide significant insights for energy retrofitting as 615 well as informing policy and potential subsidised schemes for installing more thermally performant 616 windows. 617
5. Conclusion 618
There is currently a need for models that can spatialise, quantify and estimated current and future input 619 and output material flows of building stocks in a detailed manner in order to better assess the economic 620 viability of implementing circular economy strategies for the construction sector. This paper described 621 a dynamic, stock-driven and bottom-up model which was used to quantify and map the replacement 622 flows for all buildings in the City of Melbourne, providing estimations about which materials urban 623 authorities should focus on to establish reuse and recycling strategies. Results show that replacement 624 flows represent, on average 26 kt/annum, 36 kg/(capita·annum) or 0.5 kg/(m²(gross floor area)·annum). 625 These results were found to be compatible with estimates from official waste statistics. Outputs from 626 the model can also help construction companies to assess whether material replacements represent a 627 sufficient and continuous flow of materials that could be integrated in their practices. Figures still suffer 628 from uncertainty and do not include material flows entering and exiting the stock through new 629 construction and demolition activities. Regardless, the developed model could further contribute to the 630 implementation of a more circular economy in Melbourne and be applied to other cities around the 631 world. This would allow actors of the built environment and public authorities to test different scenarios 632 and strategies in order to reduce environmental requirements associated with material replacements 633 and improve their economic value. Ultimately, this will contribute to transitioning to a more circular 634 construction sector and built environment. 635
Acknowledgements 636
This research was funded by an Early Career Researcher grant and the Graham Treloar Fellowship 637 from the Faculty of Architecture, Building and Planning, The University of Melbourne. Aristide 638 Athanassiadis was funded through a Postdoctoral Fellowship from the Université Libre de Bruxelles 639 (ULB), an FNRS mobility grant, and a WBI World excellence scholarship for his postdoctoral stay at 640 Université Paris 1 Panthéon Sorbonne. 641
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... Steel used in construction, due to the need to reduce greenhouse gas emissions, should come primarily from previous building structures; however, as research [10,17,18] shows, the reuse of steel sections is at the level of 6%, compared to 93% recycled. Obviously, we can always ask a question whether the demolition of materials and their use will be the trend of the future [19]? However, an increasingly faster transition from a linear economy (LE) [20] with high waste to a circular economy (CE) [21] based on the reuse of materials and recycling [22] is to be expected. ...
... Reusing MASH elements is one aspect of the implementation of the construction sector sustainability paradigm [17]. It should be emphasized that the implementation of the reuse of MASH elements will require changes in the market from all participants in the investment process [19]. Starting from designers, through investors, traders, contractors, users and dismantling workers. ...
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In engineering practice, investment activities related to the construction of a building are still limited to the idea of a linear cradle to grave (C2G) economy. The aim of the study is to determine the ecological and economic benefits inherent in the reuse of structural elements of a hall building using the idea of a Cradle to Cradle (C2C) looped circular economy and Life Cycle Assessment (LCA). As a rule, a multiple circulation of materials from which model buildings are made was assumed through successive life cycles: creation, use, demolition and then further use of the elements. This approach is distinguished by minimizing negative impacts as a result of optimizing the mass of the structure—striving to relieve the environment, thus improving economic efficiency and leaving a positive ecological footprint. The assessment of cumulative ecological, economic and technical parameters (EET) methodology of generalized ecological indicator (WE) for quick and practical assessment of the ecological effect of multi-use steel halls, based on LCA, was proposed. The authors of the work attempted to assess the usefulness of such a structure with the example of four types of halls commonly used in the construction industry. The linear stream of C2G (cradle to grave) and then C2C (cradle to cradle) flows was calculated by introducing ecological parameters for comparative assessment. Finally, a methodology for calculating the ecological amortization of buildings (EAB) was proposed. The authors hope that the proposed integrated assessment of technical, economic and ecological parameters, which are components of the design process, will contribute to a new approach, the so-called fast-track pro-environmental project.
... In addition to the survey, SBC has also collected 42 CBE cases studies from Asia, Africa and Latin America exploring their relationship with the SDGs. The importance of key SDGs: 6,7,8,9,11,12, 13 are compared between Europe and Global South and also between Asia and Africa. Five global core CBE indicators and nine secondary CBE indicators are selected based on the survey and their priorities compared between Europe and the Global South as well as between Asia, Africa and Latin America. ...
... In another study that considered replacement of various building materials in Melbourne, Australia, it was found that plasterboard, timber and ceramics had the greatest annual replacement over the study period (2018-2030) [8]. An integral design tool for circular building components was the focus of a study [9] where five steps were used to design and test the tool. ...
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This paper presents results from an empirical study about the interplay between circular built environment (CBE) and the 2030 Agenda for Sustainable Development. A global survey was deployed by the One Planet Sustainable Buildings and Construction Programme (SBC). This survey focused on how CBE is reflected in Sustainable Development Goals (SDGs) and the key indicators arising. The results of this survey, based on 185 responses is shared in this paper. From amongst the range of global responses, 56 were received from Europe and 96 from the Global South comprising Asia, Africa and Latin America. In addition to the survey, SBC has also collected 42 CBE cases studies from Asia, Africa and Latin America exploring their relationship with the SDGs. The importance of key SDGs: 6, 7, 8, 9, 11, 12, 13 are compared between Europe and Global South and also between Asia and Africa. Five global core CBE indicators and nine secondary CBE indicators are selected based on the survey and their priorities compared between Europe and the Global South as well as between Asia, Africa and Latin America. The results verify the importance of the SDGs in supporting circular outcomes for the built environment.
... Ismael and Shealy (2018a), Stephan and Athanassiadis (2018) Step#1: By using the linguistic scale in Table 4. the experts are asked to rank the importance of the given criteria. Moreover, the average value of the criterion is determined, and the criteria are assigned ranks and sorted as per their significance. ...
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The construction industry (CI) is responsible for consuming 3 billion tons of natural raw materials annually. Also, as per the survey by World Resources Institute, the CI accounts for 40 % of the total waste generated globally. The solution to this inefficient resource usage and adverse effects on the ecosystem is implementing Circular Economy (CE) practices in CI. However, the concept of circular construction is in developmental stages. Therefore, it is more prone to damaging risks than traditional construction. The primary aim of this study is to identify and assess the risk related to implementing CE practices in developing country construction sector. To achieve this aim, 25 risks were shortlisted from the literature review and evaluated upon the probability, detection, and severity risk criteria. This study proposed a novel hybrid fuzzy Multi-Criteria Decision Making (MCDM) approach to analyze the shortlisted risks. Fuzzy Step Wise Assessment Ratio Analysis (FSWARA) is employed to gauge the risk criterion weightage. Moreover, Fuzzy VIKOR (FVIKOR) is used to determine the risks' ranking as per the weightage of the risk criterion. The analyses ranked "lack of political support and incentives for circular construction", "difficulty in selection of circular construction experts", "profit uncertainty", and "circular material quality" as the most critical risks. Therefore, it is recommended for legislative authority to devise a framework that promotes and provides support to circular construction. Moreover, this study fills the literature gap by assessing the risks of CE practices in the CI of Pakistan.
... Understanding material flows at a neighborhood and city level is necessary for a detailed circularity assessment and decision making on an urban scale. In [16], material inflows and outflows were quantified and mapped to support decision-making for circular practice using Melbourne, Australia, as a case study. Stephan and Athanassiadis defined 48 different building archetypes based on land-use, building age, height, and their associated building assemblies and used known material replacement rates to map the future material flows into the GIS, noting parameter uncertainty and model uncertainty as some of the limitations of their model. ...
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Building stocks in cities have consumed a large amount of energy and resources globally, resulting in serious greenhouse gas pollution and environmental problems. As a circular economy has become one of the effective solutions to current environmental problems, energy and material circularity indicators of building stocks in cities become important instruments for city planners in creating sustainable and resilient cities. However, such evaluation requires a high level of integration of both spatial and attribute information of both buildings and cities. A highly integrated system is required to produce reliable analysis results. This study aims to create an evaluation tool for city planners through utilizing information contained via Building Information Modeling and Geographic Information System. Through this tool, planners and decision makers can understand current and future circularity and environmental impacts of building stocks in cities and therefore can propose the most suitable planning and governance strategies and policies. Through visualization of simulation results on an information platform, the awareness of a circular city could also be raised. Taipei city and its city master plan are used as a case study for validation of the proposed tool.
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