SUPPORTING INFORMATION FOR: Scenarios for anthropogenic copper demand and supply

in China: implications of a scrap import ban and a circular economy transition

1. Copper demand scenarios

Figure S1 Projected population and GDP of China up to 2100

Buildings: Based on previous research (Hu et al., 2010), the per capita floor area of Chinese

residential buildings is projected to change only minimally from 2050 to 2100. From 2016 to

2050, we assume the per capita floor area of Chinese urban and rural residential buildings will

increase to 52 m2 and 60 m2, respectively, in line with the research by Hu et al. (2010). In 2050

the copper content of urban residential buildings is assumed to be 1.1 kg/m2, following a logistic

function to reach 1.15 kg/m2 by 2100. For other calculations we refer to our previous research

(Dong et al., 2019). The Unified Standard for reliability design of building structures stipulates

that an ordinary building should have a design service lifetime of 50 years and that a building

structure with special significance should be designed for a service lifetime of 100 years

(MOHURD, 2018). We expect both the materials and maintenance of buildings and infrastructure

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to improve in the future such that their lifetime will most likely be longer. In the Circular

Economy scenario we therefore assumed an average lifetime of 60 years for both buildings and

infrastructure.

Infrastructure: From 2017 to 2050, electricity generating capacity and electricity demand were

calculated based on the projections of the Renewable Energy Center of China (CNREC, 2017).

To arrive at figures through to 2100, it was assumed GDP to be the sole driver. We established an

SGompertz relation between the generating capacity and GDP by means of a regression with

GDP and the generating capacity data from the National Bureau of Statistics of China (NBSC),

which shows that the R2 of the relation is 0.9899. The relation between electricity demand and

GDP by means of an SGompertz regression with GDP and the NBSC electricity demand data

yielded an R2 of 0.9976. Other categories of infrastructure were assumed to follow the same

SGompertz regression, yielding figures of 0.9973, 0.9964, 0.9933 and 0.9955, respectively, for

the R2 of electronic communication, street light, traffic light and rail metro systems. Charging

infrastructure was assumed to follow the relation between the stock of charging stations and the

number of new energy vehicles from 2005 to 2050.

Transportation: Schipper et al. (2018) project that, per capita, the world will have 0.44 cars and

0.003 buses in 2100. Based on standing Chinese policy, these figures are projected to be around

0.44 and 0.006 in China in 2050. Proceeding from the average global growth trend, and assuming

a SGompertz regression curve, these will rise to 0.67 and 0.0088 in 2100. Other categories of

transport have been assumed to follow roughly the same growth trend from 2005 to 2050.

Consumer durables, commercial durables, agricultural & industrial durables: For lack of

relevant data we had to make our own assumptions here, assuming basically that all categories

will follow an SGompertz growth curve based on the data from 2005 to 2050. Lifetime is a

crucial variable in this modelling. Several studies have investigated the average lifetimes of

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buildings, infrastructure, transportation, consumer durables and equipment for different countries

or regions (Table S2). Given the current situation in China, however, the lifetimes of certain

categories are below this level (Table S1).

Figure S2 SF distribution of copper products

Table S1 Copper content, average lifetime and standard deviation used for calculation of future copper

demand in Continuity Policy scenario and Circular Economy scenario

CategoryCopper content Lifetime (years) Standard

deviationValue Unit Source CP scenario

CE scenario in 2100

Urban residential building

0.7-1.21 with regression model

kg/ m2 f1, f2

35 60 23%

Rural residential building

0.35-0.6 with regression model 20 60 23%

Service building 0.71-0.6 with regression model 40 60 23%

Electricity generation

fossil fuels 1.0

kg/kw f3, f4 30 60 23%

biowaste 1.5nuclear 1.0hydro 1.0geothermal 1.5solar 2.0wind 1.5pumped storage 1.0

Electricity transmission 0.00041 kg/kwh f3, f4 30 60 23%

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39

Electro communication 890 kg/m³ f5 30 60 23%

Street light 890 kg/m³ f5

25 30 20%Traffic light 12 15 20%

Railmetro system

subways, ordinary-speed rail

6.06 tonne/km f6 30 40 20%

high-speed rail 8.07

Charging infrastructurepoint 20

kg/unit f7 15 20 20%station 15000Conventional car 15 kg/unit f8 12 18 20%Conventional bus 109 kg/unit f9 12 18 20%

NEV car 75 kg/unit f10 13 18 20%bus 250Truck 20 kg/unit f11 12 18 20%

Train

freight-rail cars 115

kg/unit f12 25 30 20%passenger, rolling 1299

locomotive 2960Motorcycle 1.55 kg/unit f13 12 18 20%Air conditioner 8.19 kg/unit f14, f15 13 18 26.6%Refrigerator 2 kg/unit f16 12 18 26.6%Washing machine 1.8 kg/unit f16 10 15 26.6%TV 0.52 kg/unit f12 9 13 26.6%Microwave 0.9 kg/unit f12 9 13 26.6%Heater 0.01 kg/unit f12 8 13 26.6%Cellphone 0.0003 kg/unit f15 4 6 26.6%Landline 0.10 kg/unit f15 8 10 26.6%Range hood 0.2 kg/unit f17 14 18 26.6%Computer 0.08 kg/unit f12, f15 7 10 26.6%

Commercial durablesprinters 0.18

kg/unit f15 8 15 26.6%landlines 0.10fax machines 0.10

Agricultural machines

L tractor 25.72

kg/unit f18 20 25 20%

S tractors 25.72threshing 28.98combines 29.39sprayers 22.86harvesters 28.98balers 22.45mowers 16.74

Industrial machines

excavator 43

kg/unit f18 15 25 20%loader 53bulldozer 23crane 39fork-lift 23

Data source: f1 (Bleischwitz and Nechifor, 2016), f2 (Macquarie, 2015), f3 (Fizaine and Court, 2015), f4 (García-

Olivares et al., 2012), f5 (Ministry of Communications of China), f6 (China Academy of Railway Sciences), f7

(Nansai et al., 2001), f8 (Vidal, 2017), f9 (Jin Rui Futures, https://www.jrqh.com.cn/En), f10 (International Copper

Association), f11 (van Beers, 2003), f12 (Schipper et al., 2018), f13 (Rauch et al., 2007), f14 (Oguchi et al., 2008), f15

(Oguchi et al., 2011), f16 (Truttmann and Rechberger, 2006), f17 (Ling et al., 2012), f18 (Al-Rawahi and Rieber, 1991)

Table S2 Average lifetimes of end-use sectors in different countries or regions

Categories China Japan Korea India ASEAN North America Europe Global

Infrastructure 32.5a, 10~80b, 30~35d, 12~30h 34.5a 20~33.5g 20~34a 20-33.5g 75a, 50f 60a,

35~40c 30e

Transportation 17a,10~25b, 13~25d,12~25h 9, 13a 15g 15a 15g 15a, 10~30f 13a,

13~25c17.5~40e

Consumer durables 21.5a, 8~15d, 4~14h 12.1a 16.8g 16.8a 16.8g 15a, 10~17f 16a,

8~13c 4~13e

Equipment 17.5a, 10~30b, 15~20d, 8~20h 12.1a 14.8g 14.8a 14.8g 30a, 20f 15a,

15~20c 8e

Building 32.5a, 19~40b, 30d, 20~40h, 15~75i 28.9a 28g 26a 26g 75a, 25~40f 60a, 40c 30e

“Equipment” includes commercial durables and agricultural & industrial durables.

Refs. : a (Hatayama et al., 2010), b (Zhang et al., 2015), c (Soulier et al., 2018a), d (Soulier et al., 2018b), e

(Schipper et al., 2018), f (Spatari et al., 2005), g (Yoshimura and Matsuno, 2018), h (Dong et al., 2019), i (Hu et al.,

2010).

2. Modelling China’s future copper cycle

Domestic secondary production-EoL scrap.

FL3 , p ,t=Fg, p ,t × L 3 (1)

Fh , p ,t=Fg , p ,t−FL3 , p ,t (2)

F i , p ,t=Fh , p , t× CRt (3)

F j , p , t=F i , p ,t × PRt (4)

F j ,t=∑p=1

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(F¿¿ i , p , t × PR t)¿ (5)

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where FL3 , p ,t is future dissipation or abandoned copper of different EoL products p in year t ;

Fg , p ,t is future copper scrap generation of different EoL products in year t ; L 3p is loss rate of

dissipation or abandoned copper of different EoL products p ; Fh , p ,t is future copper content in

different EoL products in year t ; F i , p ,t is collectable copper from different EoL products p in year

t ; CRt is collection rate of different EoL products p in year t , which is specific in the CP and CE

scenarios; F j , p , t is copper recycled from different EoL products p in year t ; PRt is

processing/separation rate of different EoL products p in year t , which is specific in CP and CE

scenarios; and F j ,t is total copper recycled in year t , also termed ‘old scrap’ and required to

produce secondary copper.

The “abandoned in place” stock (also is named hibernating stocks) refers to copper

applications mainly from the field of buildings and infrastructure which may remain in place after

their use phase and are not available for waste management and collection. For example, the

copper in underground cables in buildings that may not be removed after the installation of new

buildings. Thus, this temporary stock of copper is treated as a life-time extension. However, it

remains in place over the whole modeling time, analogous to dissipated copper. It is relatively

small and does not have a strong impact on the modeling results even though theoretically it

could be a source for recycling.

Domestic secondary production-New scrap.

F e, t=F f ,t+ X f ,t−M f , t (6)

Fm,t=(F e ,t

FEx ,t−Fe ,t)×(1−L 2) (7)

where F e, t is future production of copper finished products in year t ; F f , t is final demand of

copper in year t ; X f ,t and M f ,t are export and import of copper finished products in year t ,

modelled based on the relationship between GDP and the import and export volumes of copper

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finished products, as reported by Bakari and Mabrouki (2016); FEx ,t is future fabrication

efficiency in year t ; and L 2 is loss rate during new scrap collection.

Imported secondary production-imported copper scrap.

From 2013 onwards, China has implemented policies on imported copper waste, focusing

primarily on electronic waste. Imports of so-called category 7 waste were restricted as of 2017

and banned entirely in 2019, while the Chinese government plans to restrict imports of category 6

waste by 2020 and gradually implement a policy prohibiting imports of all types of copper scrap.

To estimate future imports of copper scrap following these restrictions, we first estimated

historical imports of category 6 and 7 scrap, using the following equations:

R7 , t=(GI t × ρ6−M t)/(GI t × ρ6−GI t × ρ7) (8)

M 7 ,t=GI t × ρ7× R7 ,t (9)

M 6 ,t=M t−M 7 ,t (10)

where R7 , t is the ratio of imported category 7 copper scrap in year t ; GI t is the gross weight of

imported copper scrap in year t ; and M t is the metallic weight of imported copper scrap in year t .

As imports of category 7 copper scrap were banned in 2019 from 2019 to 2100 the ratio of

imported category 7 copper scrap will be zero. ρ6 is the average copper content of imported

category 6 copper scrap, which is assumed to be 76%. ρ7 is the average copper content of

category 7, which is assumed to be 25%. These figures are based on Shanghai Nonferrous Metals

(SMM, 2019) and the China Recycling Metal Branch of the Nonferrous Metals Industry

Association (CMRA, 2006) which reported an average copper content of category 6 and 7 based

on statistical data from GACC. M 7 ,t is the metallic weight of category 7 in year t,M 6 ,t that of

category 6.

Secondary copper supply.

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Fn ,t=Fm ,t+F j , t+M w ,t−X w ,t ×(1−L 6) (11)

where Fn ,t is future secondary copper supply, M w ,t and X w ,t are future import and export of

copper scrap and L 6 is loss rate during scrap refining.

Domestic copper mining.

Fa ,t=S t × Fb ,t (12)

where Fa ,t is future domestic copper mining in year t , St is the share of domestic mining in

primary copper supply and Fb ,t is future primary copper supply in year t .

Figure S3 Historical data of domestic mining, primary copper trade and share of domestic mining in primary copper supply.

Manufacturing of semi-finished products.

F c, t=Fd , t+X s ,t−M s ,t

1−L1 (13)

where F c, t is future domestic production of semi-finished products in year t , X s ,t and M s , t are

future export and import of semi-finished products in year t and L 1 is loss rate during semis

production.

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Primary copper supply.

Fb ,t=Fc, t−Fn ,t (14)

MX cr ,t=Fb,t−Fa ,t (15)

where Fb ,t is future primary copper supply in year t and MX cr ,t is net trade of primary copper in

year t .

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Table S3 Definition and data of items used for the Chinese copper cycle

Process Symbol NameScenarios

SourceContinuity Policy scenario Circular Economy scenario

Use

S In-use stock

By stock-driven model By stock-driven model (Dong et al., 2019)

IF Infrastructure TP Transportation BI Buildings CD Consumer durables CM Commercial durables AI Agricultural & industrial durables

LT Lifetimes of copper products Regular lifetimes of copper products

Extended lifetimes of copper products Table S1

f Domestic final demandRegular demand based on stock-driven model and lifetimes

Reduced demand based on stock-driven model and lifetimes

Assumption/result in this study

Trade of finished products

MF Import of finished products Regression model Regression model Bakari and Mabrouki (2016), UN ComtradeXF Export of finished products Regression model Regression model

Fabrication

e Production of finished products Scenario-specific Scenario-specific Mass balanceEx Fabrication efficiency Average rate 89% Average rate 89% (Soulier et al., 2018b)m Scrap from fabrication (new scrap) Scenario-specific Scenario-specific Mass balanceL2 Loss during new scrap collection Scenario-specific Scenario-specific (Soulier et al., 2018b)SR Loss rate during new scrap collection 5% 5% (Soulier et al., 2018b)

Trade of semi-finished goods

MS Import of semi-finished goods Regression model Regression model (Bakari and Mabrouki, 2016), UN ComtradeXS Export of semi-finished goods Regression model Regression model

Manufacturing

c Production of semi-finished goods Scenario-specific Scenario-specific Mass balanced Semi-finished goods to fabrication Scenario-specific Scenario-specific Mass balanceL1 Loss during manufacturing Scenario-specific Scenario-specific (Soulier et al., 2018b)FR Loss rate during manufacturing 1% 1% (Soulier et al., 2018b)

Smelting & refining b Primary copper supply to manufacturing Scenario-specific Scenario-specific Mass balance

Trade of primary copper Mcr-Xcr

Net trade of copper concentrates and refined copper Scenario-specific Scenario-specific Mass balance

Domestic mining a Domestic extraction Scenario-specific Scenario-specific

Assumptions on fixed share of domestic mining in total primary copper supply (20%)

Waste management and recycling

L3 Dissipation/abandoned copper in place Category -specific Category-specific (Soulier et al., 2018b), Table S4

DR Dissipation/abandoned rate in place 1%~5%, category-specific 1%~5%, category-specific (Soulier et al., 2018b), Table S4

g Generation of EoL scrap Scenario-specific Scenario-specific Assumption/result in this study

h Total copper content in end of life (EoL) scrap Scenario-specific Scenario-specific Assumption/result in

this study

CR EoL scrap collection rate Constant value, 83%Improved value, from 83% in 2016 to 90% in 2100 with linear regression

(Soulier et al., 2018b)

i EoL copper collected Scenario-specific  Scenario-specific  Based on collection rate

PR EoL separation rate Constant value, 20%~90% (category-specific)

Improved value, to be 95% in 2100 with linear regression

(Soulier et al., 2018b), Table S4

L5 Losses during separation Based on PR Based on PR Based on PRj EoL copper recycled (old scrap) Scenario-specific  Scenario-specific  Mass balancek Collected & separated domestic scrap Based on m and i Based on m and i Based on m and iL6 Loss during scrap refining Scenario-specific  Scenario-specific  (Soulier et al., 2018b)SRR Loss rate during scrap refining 1% 1% (Soulier et al., 2018b)

n Secondary copper supply to manufacturing Scenario-specific  Scenario-specific  Mass balance

Trade of copper scrapMW Import of copper scrap Regression model Regression model (Bakari and Mabrouki,

2016), UN Comtrade

XW Export of copper scrap Regression model Regression model (Bakari and Mabrouki, 2016), UN Comtrade

Table S4 Loss rates during copper recycling in CP and CE scenarios

Categories PR-EoL Separation efficiencies (for CP

scenario)

PR-EoL Separation efficiencies

(for CE scenario in 2100)

Dissipative over

lifetime

Not-collectable

rate

Residential building 0.90 0.95 0.01 0.05

Service building 0.90 0.95 0.01 0.05

Electricity generation 0.55 0.95 0.05 0

Electricity transmission 0.55 0.95 0.05 0

Electro communication 0.55 0.95 0.05 0.05

Charging infrastructure 0.55 0.95 0.05 0.05

Rail lines & metro system 0.55 0.95 0.05 0.05

Street and traffic lights 0.55 0.95 0.05 0.05

Car 0.55 0.95 0.01 0

Bus 0.55 0.95 0.01 0

Truck 0.55 0.95 0.01 0

Trains 0.55 0.95 0.01 0

Motorcycle 0.55 0.95 0.01 0

NEV 0.55 0.95 0.01 0

Air conditioner 0.55 0.95 0.05 0

Refrigerator 0.55 0.95 0.05 0

Washing machine 0.55 0.95 0.05 0

TV 0.55 0.95 0.05 0

Microwave 0.55 0.95 0.05 0

Heater 0.55 0.95 0.05 0

Cellphone 0.55 0.95 0.05 0

Landline 0.55 0.95 0.05 0

Computer 0.55 0.95 0.05 0

Range hood 0.55 0.95 0.05 0

Commercial durables 0.70 0.95 0.05 0

Agricultural machines 0.75 0.95 0.01 0

Industrial machines 0.75 0.95 0.01 0

Figure S4 Chinese copper trade from 1996 to 2100

3. References

Al-Rawahi, K., Rieber, M., 1991. Embodied copper: Trade, intensity of use and consumption forecasts. Resources Policy 17(1), 2-12. https://doi.org/10.1016/0301-4207(91)90021-MBakari, S., Mabrouki, M., 2016. The Relationship among Exports, Imports and Economic Growth in Turkey. Munich Personal RePEc Archive. https://mpra.ub.uni-muenchen.de/76044/. Bleischwitz, R., Nechifor, V., 2016. Saturation and Growth Over Time when demand for minerals peaks. Cournot Centre. https://doi.org/10.13140/RG.2.2.24146.15049CMRA, 2006. Standard for imported copper scrap CHINA NONFERROUS METALS INDUSTRY ASSOCIATION RECYCLING METAL BRANCH, http://www.chinacmra.org/cmra/zaishengjinshuzhishi/xingyebiaozhun/list_48_2.html. CNREC, 2017. China Renewable Energy Outlook 2017. Renewable Energy Center of China. Dong, D., Tukker, A., Van der Voet, E., 2019. Modeling copper demand in China up to 2050: A business-as-usual scenario based on dynamic stock and flow analysis. Journal of Industrial Ecology. https://doi.org/10.1111/jiec.12926Fizaine, F., Court, V., 2015. Renewable electricity producing technologies and metal depletion: A sensitivity analysis using the EROI. Ecological Economics 110, 106-118. https://doi.org/10.1016/j.ecolecon.2014.12.001García-Olivares, A., Ballabrera-Poy, J., García-Ladona, E., Turiel, A., 2012. A global renewable mix with proven technologies and common materials. Energy Policy 41, 561-574. https://doi.org/10.1016/j.enpol.2011.11.018Hatayama, H., Daigo, I., Matsuno, Y., Adachi, Y., 2010. Outlook of the World Steel Cycle Based on the Stock and Flow Dynamics. Environmental Science & Technology 44(16), 6457-6463. https://doi.org/10.1021/es100044nHu, M., Bergsdal, H., van der Voet, E., Huppes, G., Müller, D.B., 2010. Dynamics of urban and rural housing stocks in China. Building Research & Information 38(3), 301-317. https://doi.org/10.1080/09613211003729988

109110111112113114115116117118119120121122123124125126127128129130131132133

Ling, Z., Zengwei, Y., Jun, B., 2012. Estimation of Copper In-use Stocks in Nanjing, China. Journal of Industrial Ecology 16(2), 191-202. https://doi.org/10.1111/j.1530-9290.2011.00406.xMacquarie, R., 2015. Copper In China-A Bottom-up Approach To Long-term Demand, Macquarie Research MOHURD, 2018. Unified standard for reliability design of building structures, 建筑结构可靠性设计统一标准. Ministry of housing and urban-rural development of the People's Republic of China. 中华人民共和国住房和城乡建设部. Nansai, K., Tohno, S., Kono, M., Kasahara, M., Moriguchi, Y., 2001. Life-cycle analysis of charging infrastructure for electric vehicles. Applied energy 70(3), 251-265. https://doi.org/10.1016/S0306-2619(01)00032-0Oguchi, M., Kameya, T., Yagi, S., Urano, K., 2008. Product flow analysis of various consumer durables in Japan. Resources, Conservation and Recycling 52(3), 463-480. https://doi.org/10.1016/j.resconrec.2007.06.001Oguchi, M., Murakami, S., Sakanakura, H., Kida, A., Kameya, T., 2011. A preliminary categorization of end-of-life electrical and electronic equipment as secondary metal resources. Waste management 31(9-10), 2150-2160. https://doi.org/10.1016/j.wasman.2011.05.009Rauch, J., Eckleman, M., Gordon, R., 2007. Copper in-use stock and copper scrap in the state of Connecticut, USA. Schipper, B.W., Lin, H.C., Meloni, M.A., Wansleeben, K., Heijungs, R., van der Voet, E., 2018. Estimating global copper demand until 2100 with regression and stock dynamics. Resources Conservation and Recycling 132, 28-36. https://doi.org/10.1016/j.resconrec.2018.01.004SMM, 2019. What are the standards for scrap copper to resources? Based on the analysis and prediction of "category six " of copper scrap Shanghai nonferrous Metals, https://www.smm.cn/. Soulier, M., Gloser-Chahoud, S., Goldmann, D., Espinoza, L.A.T., 2018a. Dynamic analysis of European copper flows. Resources Conservation and Recycling 129, 143-152. https://doi.org/10.1016/j.resconrec.2017.10.013Soulier, M., Pfaff, M., Goldmann, D., Walz, R., Geng, Y., Zhang, L., Espinoza, L.A.T., 2018b. The Chinese copper cycle: Tracing copper through the economy with dynamic substance flow and input-output analysis. Journal of Cleaner Production 195, 435-447. https://doi.org/10.1016/j.jclepro.2018.04.243Spatari, S., Bertram, M., Gordon, R.B., Henderson, K., Graedel, T.E., 2005. Twentieth century copper stocks and flows in North America: A dynamic analysis. Ecological Economics 54(1), 37-51. https://doi.org/10.1016/j.ecolecon.2004.11.018Truttmann, N., Rechberger, H., 2006. Contribution to resource conservation by reuse of electrical and electronic household appliances. Resources, Conservation and Recycling 48(3), 249-262. https://doi.org/10.1016/j.resconrec.2006.02.003van Beers, D., 2003. The magnitude and spatial distribution of in-use copper and zinc stocks in Sydney Metro. The Professional Geographer. Vidal, O., 2017. Mineral Resources and Energy: Future Stakes in Energy Transition. Elsevier. Yoshimura, A., Matsuno, Y., 2018. Dynamic Material Flow Analysis and Forecast of Copper in Global-Scale: Considering the Difference of Recovery Potential between Copper and Copper Alloy. MATERIALS TRANSACTIONS 59(6), 989-998. https://doi.org/10.2320/matertrans.M2017399Zhang, L., Yang, J., Cai, Z., Yuan, Z., 2015. Understanding the spatial and temporal patterns of copper in-use stocks in China. Environmental science & technology 49(11), 6430-6437. https://doi.org/10.1021/acs.est.5b00917

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