Supporting Information for:

Dynamic material flow and stock analysis of residential

buildings by integrating rural–urban land transition: A

case of Shanghai

Xiaofeng Gaoa*, Jun Nakatania, Qian Zhangb, Beijia Huanga,c, Tao Wangd,e, Yuichi

Moriguchia

aDepartment of Urban Engineering, The University of Tokyo, 7-3-1 Hongo, Bunkyo-

ku, Tokyo 113-8656, Japan

bDepartment of Civil Engineering, University of Victoria, Victoria, BC, V8P 5C2,

Canada

cCollege of Environment and Architecture, University of Shanghai for Science and

Technology, Shanghai, China

dInstitute for Advanced Study, Tongji University, Shanghai, 200092, China

eUNEP-Tongji Institute of Environment for Sustainable Development, Tongji

University, Shanghai, 200092, China

*Corresponding author.

E-mail: xiaofeng@env.t.u-tokyo.ac.jp

Summary Information

Number of pages: 27

Figures: S1-S18

Tables: S1-S9

S1

Contents

S1. Literature review (Table S1)

S2. Administrative division and administrative upgrade (Fig. S1-S2)

S3. Major Variables and computation formula of the residential

building stock & flow analysis (Table S2-S5)

S4. Input parameters (Fig. S3-S10, Table S6-S8)

S5. Residential building stock & floor area per capita (central urban

areas), and population composition (Shanghai) (Fig. S11)

S6. Construction materials stock and flow (Fig. S12-S13)

S7. Sensitivity analysis (Fig. S14-S17)

S8. Scenario analysis of floor area stocks & flows (Fig. S18)

S9. Result validation (Table S9)

S2

S1. Literature reviewTable S1 Review of buildings & infrastructure stocks & flows in other countries

Reviewed paper Study scale Material analyzed MethodologyPredicted future trend (Yes or no)

(Müller, 2006) Netherlands (1900-2100) Concrete in residential buildingsDemand-driven(inflow, stock, waste flow)

Yes

(Bergsdal et al., 2007)

Norway (1900-2100) Concrete and wood in residential buildingsDemand-driven(inflow, stock, waste flow)

Yes

(Brattebø et al., 2009) Norway (1960-2050) Concrete, wood, steel, and PCBs in buildingsDemand-driven(inflow, stock, waste flow)

Yes

(Hatayama and Tahara, 2016)

Japan (1960-2050) Steel, aluminum, copper and zinc in buildingsDemand-driven (inflow, stock, waste flow)

Yes

(Pauliuk et al., 2014)25 cities in 5 countries (2008)

Concrete, iron, plastics in water pipelinesStatic bottom-up (stock)

No

(Wiedenhofer et al., 2015)

EU-25 (2004–2009,2020)Nonmetallic minerals, asphalt, bitumen in residential buildings, road and railway

Static bottom-up(inflow, stock, waste flow)

Yes

(Arora et al., 2019) Singapore (2010-2016) Concrete and steel in residential buildingStatic bottom-up(stock, waste flow)

No

(Pauliuk et al., 2013) Global (1990-2008) SteelStatic top-down (stock)

No

(Fishman et al., 2014) Japan, USA (1930-2050)Timber, nonmetallic minerals, iron in buildings and infrastructure

Static top-down (Stock, waste flow)

Yes

(Tanikawa and Hashimoto, 2009)

Wakayama in Japan (1855–2004); Manchester

Construction materials in buildings, roads, railways, and water pipelines

GIS No

S3

Reviewed paper Study scale Material analyzed MethodologyPredicted future trend (Yes or no)

in UK (1984–2004) (stock)

(Tanikawa et al., 2015)

Japan (1945–2010)Construction materials in buildings, roads, railways, water pipelines and airports, seaports, and dams

GIS(stock)

No

(Kleemann et al., 2017)

Vienna in Austria (1918-2013)

Construction materials in buildingsGIS(stock)

No

(Oezdemir et al., 2017)

Rhine-Ruhr Area in Germany (2008-2015)

Construction materials in residential buildingsGIS(stock)

No

(Schebek et al., 2017)Rhine-Ruhr Area in Germany (2011)

Construction materials in non-residential buildingsGIS(stock)

No

(Miatto et al., 2019)Padua in Italy(1902-2007)

Construction materials in buildingsGIS(stock, waste flow)

No

(Mesta et al., 2019)Chiclayo in Peru (1981-2017)

Concrete, brick, steel ,mortar in residential buildings

GIS(stock)

No

Table S1 (continued)

S4

S2. Administrative division and administrative upgrade

Fig. S1. The administrative division in Shanghai.

Note: 1.RULT zones=Rural–urban land transition zones. 2. Central urban refers to urban districts since 1950, which almost have no administrative upgrade (Fig. S2). Thus, the rural–urban land transition in central urban was negligible. 3.The Pudong new area was established in 1992, so it didn’t be included in the central urban areas.

Fig. S2. The administrative upgrade during the urbanization process in Shanghai. Date source: Shanghai Statistical Yearbook (SMBS, 2017). In Shanghai, administrative upgrade mainly happened in suburban areas. In particular, there was a fast upgrade from 1992 to 1995.

S5

S3. Major variables and computation formula of the residential

building stock & flow analysisTable S2 The major variables and mathematical equations of residential building stock-flow in rural areas.

Variable Description Computation formula Note

Nr(t) Total newly constructed floor area (inflow) in rural areas

Nr(t)=FRR(t)-FRR(t-1)+Dr(t)

FRR(t) Floor area stock of rural residential (RR) building

FRR(t)=Fr(t)+¿FT(t)

Fr(t) Floor area stock of residential building in rural areas

Fr(t)=Pr(t)･fr(t) Pr is the population in rural areas, Shanghai. fr is the floor area per capita in rural areas. The history data cited from Shanghai Statistical Yearbook, and it follows a logistical function.

FT(t) Total transition stock of residential buildings due to the administrative upgrade from rural to urban

FT(t)=∑1978

t '

ST(t ' )Administrative upgrade (rural–urban land transition period) (1978≤ t ' ≤ 2003)

ST(t) Annual transition amount of residential buildings due to the administrative upgrade from rural to urban in year t

ST(t)=pT(t)･fr(t)

pT(t) Annual decreased (alteration of resident population) amount of the rural population due to the administrative upgrade from rural to urban in year t

pT(t)=Pr(t-1)･(1+B(t)-D(t))- Pr(t)

Pr is the rural population; B is the birth rate, D is the death rate in Shanghai

Pr(t) Population in rural areas of Shanghai

Pr(t)= P(t)･xr(t) P(t) is the total population in Shanghai, cited from Shanghai Statistical Yearbook; xr(t) is the proportion of population in rural areas of Shanghai. xr=1-xu, xu=urbanization level

Dr(t) Total demolished floor area (outflow) in rural areas

Dr(t)= Dr,a(t)+ Dr,b(t)+ Dr,c(t) The building constructed in year ta, tb,tc.

Dr,a(t)

Dr,b(t)

Dr,c(t)

Demolished floor area of buildings constructed before 1978 in rural areas;Demolished floor area of buildings constructed during 1978-2003 in rural areas;Demolished floor area of

(1) Dr,a(t)=

∑ D j(t−t ¿¿a)¿･φr,a(t)

(2) Dr,b(t)=

∑ D j(t−t ¿¿b)¿･

L j(t-t') is the probability distribution function of building’s lifetime.

(1) ta＜1978; (2)1978≤tb≤2003;

(3) 2003＜tc.

S6

Variable Description Computation formula Note

buildings constructed after 2003 in rural areas

φr,b(t)(3) Dr,c(t)=

∑ D j(t−t ¿¿c)¿･φr,c(t)

φr(t) Residual rate of newly constructed buildings in rural areas

(1) φr,a(t)= ∏1978

t a≤ 2003

qr(t );

(2) φr,b(t)= ∏1978≤ tb

tb ≤ 2003

qr(t );

(3) φr,c(t)=1

(1) Buildings constructed before 1978;(2)Buildings constructed during 1978-2003;(3) Buildings constructed after 2003.

qr(t) Proportion of floor area stock in rural areas in “total stock” (ST(t)+Fr(t)) in year t

qr(t)= 1-qT(t)

qT(t) Proportion of the annual decreased amount in “total stock” (annual decreased amount ST(t) plus Fr(t) floor area stock in rural areas) in year t

qT(t)=ST (t)/(ST ( t )+F r ( t ))ST(t) is the annual decreased amount of the rural residential buildings due to the administrative upgrade in year t; Fr(t) is Floor area stock of residential building in rural areas.

D j(t) Total of demolished floor area of RR building (j) in rural areas and RULT zones

Dj(t)=∑t'<t

N j (t' )･L j (t−t' ) L j(t-t') is the probability distribution function of building’s lifetime. j=AW, BW, RC, BC

N j(t )Newly constructed floor area (inflow) of building category (j)

Nj(t)= Nr(t)･p j(t) pj(t) is the proportion of buildings made by structure type j in year t.

L j(t-t') Lifetime probability distribution of building category (j)

L j (t−t ' )=lognormal(t−t' , τ j (t ' ) ,σ j2 ( t ' ))

It follows Log-normal distribution.

σ j2 (t ' )=ln (1+v j

2 ( t ' )/ E j2 (t '));

τ j ( t' )=ln [ E j( t ' )]−0.5 ln [1+v j2 (t ' )/ E j

2(t ' ) ]

. E j(t ' ) and v j ( t' )are the mean and

standard deviation of the lifetime of the building category (j) constructed at

time t' . The v j ( t ' )=0.3∙ E j(t ').

Minj(t) Construction materials

embodied in newly constructed buildings

M ¿j (t )=∑

j , αN j (t ) ∙m j , α (t ) m j , α (t) is the intensity of

construction material type α in building category (j) constructed in

S7

Variable Description Computation formula Note

year t.Mout

j(t) Construction materials embodied in demolished buildings

M outj ( t )=∑

j ,α∑t '<t

M ¿j (t ' ) · L j ( t−t' )L j(t-t') means the probability that

building constructed in year t’ is demolished in year t.

Note: r = rural areas; RR = rural residential building. RULT zones = rural–urban land transition zones.AW = adobe wood; BW = brick wood; BC = brick concrete; RC = reinforced concrete.

S8

Table S3. The major variables and mathematical equations of residential building stock-flow in the original sites of RULT zones.

Variable Description Computation formula Note

No(t) Total rebuilt floor area (inflow) in the original sites of RULT zones

No(t)= DT(t)+ Do(t)

DT(t) Total demolished floor area (outflow) of rural residential (RR) building in RULT zones

DT(t)=DRR(t)-Dr(t) Dr(t) is the total demolished floor area of rural residential building in rural areas in year t (the details as shown in Table S2)

DRR(t) Total demolished floor area (outflow) of rural residential (RR) building in rural areas and RULT zones

DRR(t)=∑ D j(t)j=AW, BW, RC, BC. Dj(t) is the total demolished floor area of RR building (j) in rural areas and RULT zones (the details as shown in Table S2)

Do(t) Total demolished floor area (outflow) of rebuilt residential building in the original sites of RULT zones

Do(t)=∑ D j ' (t)j'= BC, RC.

D j'(t) Demolished floor area (outflow) of building category (j') in the original sites of RULT zones

Dj'(t)=

∑t'<t

N j'

(t ' )･ L j '

(t−t ' )L j '(t-t') is the probability distribution function of residential building’s lifetime. Dj'(t) refers to the rebuilt building (upgraded to urban structure) will also be demolished based on the probability distribution function of building’s lifetime.

N j ' (t)Newly rebuilt floor area (inflow) of building category (j') in the original sites in the RULT zones

Nj'(t)= No(t)･p j'(t) pj'(t) is the proportion of residential buildings made by structure type j' in year t.

L j '(t-t') Lifetime probability distribution of building category (j')

L j ' (t−t' )=lognormal(t−t ' , τ j ' (t ' ) , σ j '2 ( t ' ))

It follows Log-normal distribution.

σ j '2 (t ' )= ln (1+v j '

2 (t ' ) /E j '2 (t '))

;

τ j ' (t' )=ln [ E j ' (t ') ]−0.5 ln [1+v j '2 (t ' )/ E j '

2 (t ' ) ]. E j '( t ') and

v j ' (t ' )are the mean and

standard deviation of the lifetime of the building category (j') constructed at time t

S9

' . The v j ' (t ' )=0.3 ∙ E j ' (t ' )

.

Minj'(t) Construction materials

embodied in newly rebuilt buildings

M ¿j ' (t )=∑

j' , αN j'

( t)∙ m j' ,α (t ) m j ' , α(t ) is the intensity of construction

material type α in building category (j') constructed in year t.

Moutj'(t) Construction materials

embodied in demolished buildings

M outj' (t )=∑

j' , α∑t'<t

M ¿j'

( t ' )∙ L j '

(t-t')

L j '(t-t') means the probability that building constructed in year t' is demolished in year t.

Note: BC = brick concrete; RC = reinforced concrete. UR = urban residential building. RULT zones = rural–urban land transition zones.

S10

Table S4. The major variables and mathematical equations of urban residential (UR) building stock-flow in the new sites of RULT zones

Variable Description Computation formula or data source Note

NNR(t) Newly constructed floor area (inflow) of UR building in the new sites of RULT zones

NNR(t)=NUR(t)—NCR(t) NCR(t) is the total newly completed floor area (inflow) of residential building in central urban areas (see details in Table S5)

NUR(t) Total newly constructed floor area (inflow) of UR building

NUR(t)= FUR(t)-FUR(t-1)+DUR(t)

FUR(t) Total floor area stock of urban residential (UR) building

FUR(t)=Fu(t)−¿FT(t) FT(t) is the total decreased amount of residential buildings in rural areas due to the administrative upgrade (see details in Table S2).

Fu(t) Floor area stock of residential building in urban areas

Fu(t)=Pu(t)･fu(t) fu is the floor area per capita in urban areas. The history data cited from Shanghai Statistical Yearbook, and it follows a logistical function.

Pu(t) Population in urban areas of Shanghai

Pu(t)= P(t)･xu(t) P(t) is the total population in Shanghai, cited from Shanghai Statistical Yearbook; xu(t) is the proportion of population in urban areas of Shanghai. xu=urbanization level

DNR(t) Demolished floor area of newly constructed residential buildings in new sites of RULT zones

DNR(t)=DUR(t)－DCR(t) DCR(t) is the total demolished floor area (outflow) of residential building in central urban areas (see details in Table S5)

DUR(t) Total demolished floor area (outflow) of UR building

DUR(t)= ∑j ' '

D j' '

( t ) j' '=RC, BC

D j' '(t) Demolished floor area (outflow) of building category (j' ') in new sites of RULT zones

Dj' '(t)=∑t'<t

N j' '

(t ' )･L j' '

(t−t ') L j ' '(t-t') is the probability distribution function of building’s lifetime.

N j' ' (t)Newly completed floor area (inflow) of building category (j' ')

Nj' '(t)= NUR(t)･p j' '(t) p j' '(t) is the proportion of buildings made by structure type j' ' in year t.

L j ' '(t-t') Lifetime probability distribution of building category (j' ')

L j ' ' (t−t ' )= lognormal(t−t ' , τ j ' ' (t' ) , σ j ' '2 ( t ' ))

It follows Log-normal distribution.

σ j ' '2 (t ' )=ln (1+v j ' '

2 (t ' )/ E j' '2 (t ' ));

τ j ' ' (t ')=ln [ E j ' ' (t ')]−0.5 ln [1+v j ' '2 (t ' )/ E j ' '

2 (t ' )]

S11

. E j ' ' (t ') and v j ' ' (t ')are the mean and

standard deviation of the lifetime of the

building category (j' ') constructed at time t'

. The v j ' ' (t ' )=0.3 ∙ E j ' '(t ').

Minj' '(t) Construction materials

embodied in newly built buildings

M ¿j' ' (t )=∑

j ' ' ,αN j' '

(t )∙ m j' ' ,α (t ) m j ' ' ,α (t) is the intensity of construction

material type α in building category (j' ') constructed in year t.

Moutj' '(t) Construction materials

embodied in demolished buildings

M outj' ' (t )=∑

j ' ' ,α∑t '<t

M ¿j' '

(t ' ) ∙ L j' '

(t−t ' ) L j ' '(t-t') means the probability that building constructed in year t' is demolished in year t.

Note: 1.BC = brick concrete; RC = reinforced concrete. 2.RULT zones = rural-urban land transition zones. 3.UR = urban residential building.

S12

Table S5. The major variables and mathematical equations of urban residential (UR) building stock-flow in central urban areas.

Variable Description Computation formula or data source Note

NCR(t) Total newly constructed floor area (inflow) of residential building in central urban areas

NCR(t)= FCR(t)-FCR(t-1)+DCR(t)

FCR(t) Floor area stock of residential building in central urban areas

History data: Shanghai Statistical Yearbook

Due to the limited land space in central urban areas, and the population & floor area per capita in central urban areas kept stable in recent years already (see details in Fig. S11). Therefore, it is assumed that the stock in central urban areas will keep stable during 2018-2100.

DCR(t) The total demolished floor area (outflow) of residential building in central urban areas

DCR(t)= ∑j' ' '

D j' ' '

( t ) j' ' '=RC, BC

D j' ' '(t) Demolished floor area (outflow) of building category (j' ' ')

Dj' ' '(t)=

∑t'<t

N j' ' '

(t ' )･L j ' ' '

(t−t ' )L j ' ' '(t-t') is the probability distribution function of building’s lifetime.

N j' ' '( t)Newly completed floor area (inflow) of building category (j' ' ')

Nj' ' '(t)= NCR(t)･ p j' ' '(t) pj' ' '(t) is the proportion of buildings made by structure type j' ' ' in year t.

L j ' ' '(t-t') Lifetime probability distribution of building category (j' ' ')

L j ' ' ' (t−t ' )=lognormal( t−t' , τ j ' ' ' (t ') , σ j ' ' '2 ( t ' ))

It follows Log-normal distribution.

σ j ' ' '2 ( t ' )= ln (1+v j ' ' '

2 ( t ' )/ E j ' ' '2 (t '));

τ j ' ' ' ( t ' )=ln [E j ' ' ' (t ' )]−0.5 ln [1+v j ' ' '2 ( t' )/ E j ' ' '

2 (t ')]

. E j ' ' ' (t ') and v j ' ' ' ( t ' )are the mean and

standard deviation of the lifetime of building category (j’’’) constructed at

time t’. The v j ' ' ' ( t ' )=0.3∙ E j ' ' ' (t ' ).

Minj' ' '(t) Construction materials

embodied in newly built buildings

M ¿j ' ' ' (t )=∑

j' ' ' ,αN j ' ' '

(t) ∙m j' ' ' , α (t ) m j ' ' ' , α (t) is the intensity of

construction material type α in building category (j' ' ') constructed in year t.

Moutj' ' '(t) Construction materials

embodied in M out

j ' ' ' (t )=∑j' ' ' ,α

∑t'<t

M ¿j ' ' '

(t ' )∙ L j' ' '

(t−t ') L j ' ' '(t-t') means the probability that building constructed in year t' is

S13

demolished buildings demolished in year t.

Note: BC = brick concrete; RC = reinforced concrete. UR = urban residential building.

S14

S4. Input parametersS4-1. Population & urbanization level

Fig. S3. (a) the total population in Shanghai (1950-2100). Note: 1. the solid line indicates historical statistics data (NBSC, 2010b; SMBS, 1989, 2010a, b, 2017; Xie, 2010). 2. the dots are the previous studies’ estimates: medium scenario’s data (HPGWP, 2018; SUPLRAB, 2018), low (19.3) & high (30.7) scenarios’ data by 2040 (Yang et al., 2016), we extrapolated the trend to 2100.

(b) The percentage of urban population (urbanization level) in Shanghai (1950-2100). Note: the dots are the historical statistics data (NBSC, 2010b, 2018; SMBS, 1989, 2010b; Xie, 2010). 2.the solid line indicates that the estimation for urbanization level.

(c) The relation between urbanization level and GDP in Shanghai. Note:1.Historical data of GDP cited from SMBS (2017). 2.The solid line indicates that the urbanization level was assumed to follow the logistic function.

S15

(a)

(b)

(c)

S4-2. Population transition from rural areas to urban areasTable S6. The calculation procedure of population transition during administrative upgrade in Shanghai

Items Description Computation formula or data source Note

△P(t) The population changes in Shanghai

△P(t)=P(t)-P(t-1)=[i(t)-e(t)]+[b(t)-d(t)] P is the total population in Shanghai; i(t) and e(t) are the immigration and emigration of population from other provinces in year t. b(t) and d(t) are the birth and death population in Shanghai in year t.

i(t)-e(t) The net migration in Shanghai

i(t)-e(t)= [iu(t)-eu(t)]+[ir(t)-er(t)] ≈[iu(t)-eu(t)]

iu(t) and eu(t) are the immigration and emigration in urban areas; ir(t) and er(t) are the immigration and emigration in rural areas. Migration flows mainly happed in urban areas, where ir(t)-er(t)≈0.

△Pr(t) The population changes in rural areas in Shanghai

△Pr(t)=Pr(t)-Pr(t-1)=[ir(t)-er(t)]+[br(t)-dr(t)]-pT(t)≈[br(t)-dr(t)]-pT(t)

Pr is the population in rural areas. br and dr are the birth and death population in rural areas. pT (t) is the population transition in yeat t. where ir(t)-er(t)≈0.

pT(t) Population transition in year t

pT(t)≈[br(t)-dr(t)]-[Pr(t)-Pr(t-1)]

≈Pr(t-1)･[1+B(t)-D(t)]-Pr(t)

B,D refer to the birth rate and death rate in Shanghai. There is no specific for rural and urban areas. Therefore, using the same information due to the similar household size in rural, urban areas in Shanghai (as shown in Fig. S8 (b))

Fig. S4. The birth rate, death rate, and natural growth rate of population in Shanghai (1978-2002). Data source: (SMBS, 2017).

S16

Fig. S5. Population transition from rural to urban areas in each year during the administrative upgrade in Shanghai (see computational details in Table S6).

S17

S4-3. Floor areas per capita & household size

Fig. S6. Floor area per capita of residential building in (a) urban areas and (b) rural areas in Shanghai (1950-2100). Note: 1. The blue dots are the statistics data of urban floor area per capita and rural floor area per capita (SMBS, 2010a, 2017). 2. The black line is the medium scenario of urban areas. It was extrapolated by logistic function to reach a saturation value 33 m2. 3. The purple dotted line is the high scenario of urban areas. It was set 18% higher than the medium scenario, which was driven by high GDP per capita, see details in SI Fig. S7 (a). The low scenario of urban areas was set 18% lower than the medium scenario.

Fig. S7. Floor area per capita of residential building in (a) urban areas and (b) rural areas driven by GDP per capita in Shanghai. Note: 1. the blue dots are the statistics data (SMBS, 2010a, 2017); 2. the pink dots are forecasted by GDP per capita (low, medium and high scenarios) in 2040, which cited from Yang et al. (2015).

Fig. S8. (a) Floor area per capita & (b) household size in Shanghai and the whole of China. Data source: (NBSC, 2018; SMBS, 2010a, 2017)

S18

(b)(a)

(a) (b)

(a) (b)

S4-4. Proportion of newly constructed urban residential building by structural materialsTable S7. Composition of urban residential building stock in Shanghai, 2011-2017 (Million m2)

Detached house

Terrace house Apartments Modern lane houses

Old-fashioned lane houses

Simple shed

2011 22.15 4.96 502.61 5.25 12.22 0.292012 17.08 10.52 519.75 3.11 12.06 0.112013 17.52 11.65 543.19 3.11 13.82 0.112014 17.9 12.41 564.29 3.11 13.12 0.112015 17.9 14 583.08 3.08 11.90 0.112016 18.15 15.08 608.02 2.96 10.61 0.112017 18.40 15.65 624.53 3.03 11.09 0.11

Data source: (SMBS, 2017). Note:1.There are 6 types of urban residential building according to the adjustment of residential building classification provided by Shanghai Municipal Housing Security and Building Administration Bureau since 2011.2.All the “Modern lane houses”, “Old-fashioned lane houses”, and “Simple shed” were constructed before 1949. With the promotion of renovation of “urban villages” in recent years, “urban villages” be included in the “Old-fashioned lane residential blocks” since 2013.

Table S8. Information on newly constructed urban residential building in Shanghaia Increased floor area of

BC(Million m2)

b Total newly constructed floor area

(Million m2)

Proportion of newly constructed building

BC RC

2012 0.49 33.72 1.45% 98.55%2013 1.57 35.28 4.45% 95.55%2014 1.14 39.06 2.92% 97.08%2015 1.59 34.76 4.57% 95.43%2016 1.33 40.69 3.27% 96.73%2017 0.82 39.95 2.05% 97.95%

Data source: (NBSC, 2017; SMBS, 2017)Note: “a” refers to the net addition of brick concrete (BC) building (“Detached house” & “Terraced house”), which approximately equal to the newly constructed floor area. The demolition of “Detached house” & “Terraced house” be neglected during 2012-2017. It is assumed that all the newly constructed apartments are reinforced concrete (RC) building since 2012. “b” refers to the total newly constructed floor area, cited from China Statistical Yearbook on Construction (NBSC, 2017)

S19(a) (b)

Fig. S9. the proportion of newly constructed (a) urban and (b) rural residential building by structural types in Shanghai. Note: 1.The dots are the historical data (Huang et al., 2013; NBSC, 2010a, 2017; SG, 2008; SMBS, 2017). 2. AW = adobe wood; BW = brick wood; BC = brick concrete; RC = reinforced concrete.

S4-5. Lifetime of residential buildings

Fig. S10. The average lifetime of (a-b) urban and (c-f) rural residential building by structural types.Note: 1.Data source: (Cao et al., 2019; Hu et al., 2010a; Hu et al., 2010b; Huang et al., 2013; MOHURD, 2005; Ouyang, 2007; SCC, 1990; Shi et al., 2012; Song, 2004; Yang and Kohler, 2008). 2. AW = adobe wood; BW = brick wood; BC = brick concrete; RC = reinforced concrete. 3. The current average lifetime may not reflect the lifetime of young buildings in reality which constructed in the recent decade. These young buildings have not yet reached their end-of-life stage because buildings have a longer lifetime than other products (Dai et al., 2019). In order to estimate the lifetime of these young buildings appropriately,

S20

(a) (b)

(c) (d)

(e) (f)

Construction year Construction year

three scenarios were set to estimate the lifetime of UR buildings after 2005 toward 2100.

S21

S5. Residential building stock & floor area per capita (central urban

areas), and population composition (Shanghai)

Fig. S11. (a) Residential building stock & floor area per capita in central urban areas of Shanghai (2005-2017); (b) Population composition in Shanghai (2005-2017). Note: 1.The data source of residential building stock & population

(SMBS, 2017). RULT zones = Rural–urban land transition zones. 2. Due to the limited land space in central urban areas, and the population & floor area per capita in central urban areas kept stable in recent years already.

S22

(b)(a)

S6. Construction materials stock and flow

Fig. S12. Material stocks and flows of residential buildings in Shanghai (scenario: not considering rural–urban land transition-medium). Note: urban = urban areas (rural–urban land transition zones plus central urban areas); rural = rural areas..

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Rural Urban

Fig. S13. Comparison of scenario results of material inflows & stocks of residential buildings in Shanghai (C-RULT:

considering rural–urban land transition; NC-RULT: not considering rural–urban land transition appropriately). The comparison of demolition waste see details in Fig. 7.

Note: urban = urban areas (rural–urban land transition zones plus central urban areas); rural = rural areas.

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Rural Urban

S7. Sensitivity analysis

S7-1. Parameter: population

Fig. S14. Sensitivity analysis of floor area & material flows in the whole of Shanghai to the variation in population (P). Note: 1.The solid line is the medium result. The shadow indicates the results of changing the population (low & high scenario), the other input parameters set at medium values. 2. Historical data source: (MOHURD, 2017; SMBS, 2017)

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Floor areas flow Materials flow

S7-2. Parameter: floor area per capita

Fig. S15. Sensitivity analysis of floor area & material flows in the whole of Shanghai to the variation in floor area per capita (FAPC). Note: 1. The solid line is the medium result. 2. The shadow indicates the results of changing the FAPC (low & high scenario), the other input parameters set at medium values. 3. Historical data source: (MOHURD, 2017; SMBS, 2017).

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Floor areas flow Materials flow

S7-3. Parameter: average lifetime

Fig. S16. Sensitivity analysis of floor area & material flows in the whole of Shanghai to the variation in average lifetime (L). Note: 1.The solid line is the medium result. 2. The shadow indicates the results of changing the L (low & high scenario), the other input parameters set at medium values. 3. Historical data source: (MOHURD, 2017; SMBS, 2017)

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Floor areas flow Materials flow

S7-4. Parameter: material intensity

Fig. S17. Sensitivity analysis of total material flows in the whole of Shanghai to the variation in material intensity (MI). Note: 1.The solid line is the medium result. 2.The pink shadow indicates the results of changing the material intensity by +10% and -10%, and the grey shadow portrays the results of changing the material intensity by +20% and -20%, the other input parameters set at medium values.

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Materials flow

S8. Scenario analysis of floor area stocks & flows

Fig. S18. Scenarios of floor area stocks & flows of residential buildings in the whole of Shanghai (1950−2100). Note: 1.The solid yellow line is the medium result. 2.The shadow indicates the uncertainty range. 3.Historical data source: (MOHURD, 2017; SMBS, 2017). 4.(P = population; L = lifetime; F = floor area per capita). Special scenarios: Lowest scenario (Low P & Low F & Long L); Medium scenario (Medium P & Medium F & Medium L); Highest scenario (High P & High F-Short L); Sensitivity scenarios: only one parameter change at a time while keeping other parameters constant, such as P-high & P-low refers to the population set at a low or high value, the others’ parameters set as medium value). Detailed parameter settings are shown in Table 3.

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S9. Result validationTable S9. Result validation of material stocks in residential buildings (MMt)

This study Han et al. (2018)a Deviationb

2000 311 285 9.00%

2005 491 508 -3.31%

2010 760 712 6.75%

2015 936 876 6.86%

Note: a. The data of Han et al. (2018) in 2015 are provided by the paper’s corresponding author. As one of

the main raw material of concrete, sand was not included in Han et al. (2018)’ research, all of the data had

been adjusted based on the specification for mix proportion design of ordinary concrete (JGJ55-2011)

(Ding and Leng, 2011).

b. Deviation is defined as (This study - Han et al. (2018)) / Han et al. (2018) ×100%.

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ReferencesArora, M., Raspall, F., Cheah, L., Silva, A., 2019. Residential building material stocks and

component-level circularity: The case of Singapore. Journal of Cleaner Production 216, 239-248.

Bergsdal, H.v., Bratteb , H., Bohne, R.A., Müller, D.B., 2007. Dynamic material flow analysis for Norway's dwelling stock. Building Research & Information 35, 557-570.

Brattebø, H., Bergsdal, H., Sandberg, N.H., Hammervold, J., Müller, D.B., 2009. Exploring built environment stock metabolism and sustainability by systems analysis approaches. Building Research & Information 37, 569-582.

Cao, Z., Liu, G., Duan, H., Xi, F., Liu, G., Yang, W., 2019. Unravelling the mystery of Chinese building lifetime: A calibration and verification based on dynamic material flow analysis. Applied Energy 238, 442-452.

Dai, M., Wang, P., Chen, W.-Q., Liu, G., 2019. Scenario analysis of China's aluminum cycle reveals the coming scrap age and the end of primary aluminum boom. Journal of Cleaner Production 226, 793-804.

Ding, W., Leng, F., 2011. Specification for mix proportion design of ordinary concrete (JGJ55-2011). China Building Industry Press: Beijing, China.

Fishman, T., Schandl, H., Tanikawa, H., Walker, P., Krausmann, F., 2014. Accounting for the material stock of nations. Journal of industrial ecology 18, 407-420.

Han, J., Chen, W.-Q., Zhang, L., Liu, G., 2018. Uncovering the Spatiotemporal Dynamics of Urban Infrastructure Development: A High Spatial Resolution Material Stock and Flow Analysis. Environmental Science & Technology 52, 12122-12132.

Hatayama, H., Tahara, K., 2016. Using decomposition analysis to forecast metal usage in the building stock. Building Research & Information 44, 63-72.

HPGWP, 2018. City population projections for the world's 200 largest cities in 2100. HPGWP (Hoornweg & Pope's GCIF Working Paper), Available from: https://sites.uoit.ca/sustainabilitytoday/urban-and-energy-systems/Worlds-largest-cities/population-projections/city-population-2100.php (accessed 2 November 2019).

Hu, M., Bergsdal, H.v., van der Voet, E., Huppes, G., Müller, D.B., 2010a. Dynamics of urban and rural housing stocks in China. Building Research & Information 38, 301-317.

Hu, M., Pauliuk, S., Wang, T., Huppes, G., van der Voet, E., Müller, D.B., 2010b. Iron and steel in Chinese residential buildings: A dynamic analysis. Resources, Conservation and Recycling 54, 591-600.

Huang, T., Shi, F., Tanikawa, H., Fei, J., Han, J., 2013. Materials demand and environmental impact of buildings construction and demolition in China based on dynamic material flow analysis. Resources, Conservation and Recycling 72, 91-101.

Kleemann, F., Lederer, J., Rechberger, H., Fellner, J., 2017. GIS-based Analysis of Vienna's Material Stock in Buildings. Journal of Industrial Ecology 21, 368-380.

Müller, D.B., 2006. Stock dynamics for forecasting material flows—Case study for housing in The Netherlands. Ecological Economics 59, 142-156.

Mesta, C., Kahhat, R., Santa-Cruz, S., 2019. Geospatial Characterization of Material Stock in the Residential Sector of a Latin-American City. Journal of Industrial Ecology 23, 280-291.

Miatto, A., Schandl, H., Forlin, L., Ronzani, F., Borin, P., Giordano, A., Tanikawa, H., 2019. A S31

spatial analysis of material stock accumulation and demolition waste potential of buildings: A case study of Padua. Resources, Conservation and Recycling 142, 245-256.

MOHURD, 2005. Residential building code GB 50368—2005. MOHURD (Ministry of Housing and Urban-Rural Development of the People’s Republic of China), Beijing, China (in Chinese).

MOHURD, 2017. Statistical yearbook of urban and rural construction. MOHURD (Ministry of Housing and Urban-Rural Development of the People’s Republic of China), Beijing, China (in Chinese).

NBSC, 2010a. China Yearbook of rural household survey. NBSC (National Bureau of Statistics of China). China Statistics Press, Beijing, China (in Chinese).

NBSC, 2010b. Tabulation on the 1953,1964, 1982, 1990, 2000, 2010 Population Census of the People’s Republic of China. NBSC (National Bureau of Statistics of China). China Statistics Press, Beijing, China (in Chinese).

NBSC, 2017. China Statistical Yearbook on Construction. NBSC (National Bureau of Statistics of China). China Statistics Press, Beijing, China (in Chinese).

NBSC, 2018. China Statistical Yearbook. NBSC (National Bureau of Statistics of China)China Statistics Press, Beijing, China (in Chinese).Oezdemir, O., Krause, K., Hafner, A., 2017. Creating a Resource Cadaster—A Case Study of a

District in the Rhine-Ruhr Metropolitan Area. Buildings 7, 45.Ouyang, J., 2007. Study on Life Cycle of Chinese City Housing, Xi'an University of Architecture

and Technology, Xi'an, China (dissertation, in Chinese).Pauliuk, S., Venkatesh, G., Brattebø, H., Müller, D.B., 2014. Exploring urban mines: pipe length

and material stocks in urban water and wastewater networks. Urban Water Journal 11, 274-283.

Pauliuk, S., Wang, T., Müller, D.B., 2013. Steel all over the world: Estimating in-use stocks of iron for 200 countries. Resources, Conservation and Recycling 71, 22-30.

SCC, 1990. Interim Regulations of the People's Republic of China Concerning the Assignment and Transfer of the Right to the Use of the State-owned Land in the Urban Areas. SCC (The State Council of China), Beijing, China (in Chinese).

Schebek, L., Schnitzer, B., Blesinger, D., Köhn, A., Miekley, B., Linke, H.J., Lohmann, A., Motzko, C., Seemann, A., 2017. Material stocks of the non-residential building sector: the case of the Rhine-Main area. Resources, Conservation and Recycling 123, 24-36.

SG, 2008. The Development Research Center of Shanghai Municipal People's Government, Special report on the situation and recommendations of Shanghai's high-rise buildings. SG (Shanghai Government), Shanghai (in Chinese).

Shi, F., Huang, T., Tanikawa, H., Han, J., Hashimoto, S., Moriguchi, Y., 2012. Toward a low carbon–dematerialization society. Journal of Industrial Ecology 16, 493-505.

SMBS, 1989. Shanghai Compilation of Population Statistics, 1949-1988. SMBS (Shanghai Municipal Bureau of Statistics). China Statistics Press, Beijing, China (in Chinese).

SMBS, 2010a. Shanghai Compendium of Statistics, 1949-2008. SMBS (Shanghai Municipal Bureau of Statistics). China Statistics Press, Beijing, China (in Chinese).

SMBS, 2010b. Tabulation on the 2000, 2005, 2010 Population Census of Shanghai. SMBS (Shanghai Municipal Bureau of Statistics). China Statistics Press, Beijing, China (in Chinese).

S32

SMBS, 2017. Shanghai Statistical Yearbook. SMBS (Shanghai Municipal Bureau of Statistics). China Statistics Press, Beijing, China (in Chinese).

Song, C., 2004. Full life cycle, high quality. Urban Development, 4-8 (in Chinese).SUPLRAB, 2018. Shanghai Urban Planning (2017-2035). SUPLRAB (Shanghai Urban Planning

and Land Resource Administration Bureau), Shanghai, (in Chinese).Tanikawa, H., Fishman, T., Okuoka, K., Sugimoto, K., 2015. The weight of society over time and

space: a comprehensive account of the construction material stock of Japan, 1945-2010. Journal of Industrial Ecology 19, 778-791.

Tanikawa, H., Hashimoto, S., 2009. Urban stock over time: spatial material stock analysis using 4d-GIS. Building Research & Information 37, 483-502.

Wiedenhofer, D., Steinberger, J.K., Eisenmenger, N., Haas, W., 2015. Maintenance and expansion: Modeling material stocks and flows for residential buildings and transportation networks in the EU25. Journal of industrial ecology 19, 538-551.

Xie, L., 2010. Studies on population developemnt in Shanghai Since 1949. Shanghai People's publishing House (in Chinese).

Yang, R., Yang, J., Wang, X., 2015. Changes in medium and long term economic growth factors and the impact on Shanghai's economic development (in Chinese). Scientific Development 5, 5-16

Yang, W., Kohler, N., 2008. Simulation of the evolution of the Chinese building and infrastructure stock. Building Research & Information 36, 1-19.

Yang, X., Liu, J., Zhou, W., Qin, Z., 2016. Prediction for 2040 Shanghai population based on economic growth. China City, (6): 38-44. (in Chinese).

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