Understanding the Spatial and Temporal Patterns of Copper In-Use

Apr 30, 2015 - Two approaches are adopted to characterize the comprehensive pattern of the copper in-use stocks in China. The top-down results indicat...
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Understanding the Spatial and Temporal Patterns of Copper In-Use Stocks in China Ling Zhang,† Jiameng Yang,† Zhijian Cai,† and Zengwei Yuan*,‡ †

College of Economics and Management, Nanjing Forestry University, Nanjing 210037, P. R. China State Key Laboratory of Pollution Control and Resource Reuse, School of the Environment, Nanjing University, Nanjing 210023, P. R. China

‡

S Supporting Information *

ABSTRACT: Two approaches are adopted to characterize the comprehensive pattern of the copper in-use stocks in China. The top-down results indicate that both the total amount and the per capita quantity of the stocks have exhibited a significant and increasing trend for the past 60 years, especially since 2000. The topdown results show that the copper stocks increased from a negligible level of less than 1 kg/capita in 1952 to 44 kg/capita in 2012. The total stocks in 2012 are estimated to be 60 Mt by a top-down approach or 48 Mt by a bottom-up calculation. The bottom-up method determines that the largest reservoir is the infrastructure sector, which accounts for approximately 58% of the total stocks. The spatial pattern indicates that the copper in-use stocks are predominately spatially distributed in the eastern regions of China, a feature that is obviously different from the geographical distribution of the primary resources. Analysis on the prospects of stocks shows both the total magnitude and per capita value will continuously increase in the following decade, and enter a relatively stable stage in around 2030, with a maximum value of 106 kg/capita. The results improve the knowledge about closing copper cycles.

1. INTRODUCTION As the world’s second-largest economy, China consumes huge amounts of resources, such as steel, copper, aluminum, and zinc, to support its economic growth. Copper is a widely employed industrial metal in modern society, and its consumption has experienced a substantial increase during the past 60 years in China. Statistical figures derived from the Yearbook of China Nonferrous Metals Industry1,2 show that the output of refined copper increased from 0.04 Mt in 1950 to 6.06 Mt in 2012, and the domestic consumption of refined copper grew from 0.036 Mt to 7.68 Mt during the same period, increasing by 150 and 212 times, respectively. This implies a continuous and substantial shift of copper from the lithosphere into anthropogenic stocks, in the form of various coppercontaining products, retained as copper in-use stocks. There has been growing interest in understanding these in-use stocks in recent years because the stocks can represent the future potential of copper recovery3 and provide implications for developing a circular economy.4 Previous studies have quantified copper in-use stocks using either a top-down approach based on an estimation of annual consumption and product-lifetime assumptions5 or a bottomup approach based on the quantities of products in use and their copper intensities.6,7 In China, research on copper in-use stocks has also aroused interest in recent years, and studies, particularly those at city level, show that the per capita stocks range between 30 and 50 kg.7,8 These studies, however, focus on a specific city and a specific year, merely providing a static © XXXX American Chemical Society

snapshot. The quantity, quality, location, and historical patterns of the copper in-use stocks are hitherto still poorly understood at the country level. This paper first identifies the historical patterns of the copper in-use stocks in China based on a top-down method, which is useful for extrapolating future stocks; hence, the future demand of copper can be projected to some extent. The copper in-use stocks for the year 2012 are then characterized with the bottom-up method. Several questions are addressed in the research: (1) What are the historical patterns of copper in-use stocks as societies evolve? (2) After more than 60 years of growing demand and production, where is the copper currently in use (in which areas, in what subsystems, and how much)? (3) Based on the historical patterns and status quo of the stocks, what will happen to copper in-use stocks in future?

2. METHODOLOGY 2.1. Top-Down Quantification of Copper In-Use Stocks. The anthropogenic cycle of copper consists of four life stages: production, fabrication and manufacturing, societal use, and waste management.9 Specifically, copper mined from the lithosphere is transformed into cathode copper (also called refined copper) and then converted into semifabricated Received: August 11, 2014 Revised: April 30, 2015 Accepted: April 30, 2015

A

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2.1.2. Determination of Fi,tout. The Weibull statistical distribution is adopted in this study to model the lifetime of copper-containing products (di,k), which has been applied to previous studies for stock and obsolescence calculations.10,11 The Weibull random variable t is characterized by a location parameter a, a scale parameter α, and a shape parameter β. The probability density function (pdf) of the distribution is given by eq 4. Details of data processing and data sources are provided in the SI.

products, produced and/or assembled into intermediate products and final products. These products then enter societal use and stay in use for their entire life times and are eventually discarded (partly to be reused). At the stage of “societal use”, the in-use stocks refer to the copper currently providing various types of services to the population. The stocks are generated when more copper is entering than departing the use stage. The change in the in-use stocks over a given time t is the difference in the value between the copper product inflow and the obsolete product outflow. The obsolete outflow at time t can be expressed as a function of the product inflows before time t and the characteristic obsolescence behavior of the product. Mathematically Si , t = Fiin, t − Fiout , t + Si , t − 1

⎧ ⎛ t − a ⎞α ⎪ −α α − 1 −⎜⎝ β ⎟⎠ αβ ( t − a ) e , if t > a f (t ; a , α , β ) = ⎨ ⎪ ⎩0 otherwise (4)

(1)

2.2. Bottom-Up Characterization of the Copper In-Use Stocks. Bottom-up studies directly measure the stocks in use at a discrete point in time in terms of the metal contained in the technologies.12 In its simplest form, estimating in-use stocks via the bottom-up method is represented by

b

Fiout ,t =

∑ (Fiin,t− k × di ,k)

(2)

k=1

where Si,t and Si,t−1 are the cumulative in-use stocks for coppercontaining product i in year t and t −1, respectively; Fini,t and Fini,t−k are the flows of copper in new goods into sector i in year t and year t − k, respectively; Fout i,t is the end-of-life copper scrap arising from sector i in year t; b is the maximum lifetime of the copper-containing product; and di,k is the lifetime-distribution density value of the product i, which can be represented as a probability density function (pdf). 2.1.1. Determination of Fin i,t. In this research, the total copper flows entering into society (Fint ) are approximated as the ined apparent consumption of refined copper (Fcons,ref ) plus the t net−impo,semis net imports of semifabricated copper (Ft ) and the inished finished products (Fnet−impo,f ), as shown in eq 3: t Ftin = Ftcons , refined + Ftnet − impo , semis + Ftnet − impo , finished

A

St =

i

(3)

represents the domestic production of coppercontaining products. This choice is made because the data for refined copper can be acquired directly (deemed as pure copper), and do not need equivalent transforming. Once refined copper is utilized in manufacturing, usually in combination with other materials, data are available only in the form of products or groups of products, for which the copper content must be estimated. The data about Ftin, ined net−impo,semis inished Fcons,ref ,Ft , and Fnet−impo,f are described in the t t Supporting Information (SI, Tables S1−S3). The flow of Fint is then branched into four main end-use categories (Fini,t): infrastructure, transportation, buildings, and equipment. Each category contains subcategories,7 but they are simplified as a result of deficient information and to simplify the calculation. Table 1 shows the end-use copper-containing product groups and the branching ratios used for each decade (cf. SI Table S4 for more details on the data and data sources). Table 1. Branching Ratios for Copper End Uses in China (by the Top-Down Approach) infrastructure

transportation

equipment

buildings

45 39 38 50 46 45

1 1 1 5 11 11

53 59 60 40 34 34

1 1 1 5 9 10

(5)

where St represents the total in-use stocks of copper at time t, Ni,t is the quantity of the final product i in use at time t, mi,t is the copper content of the in-use final product i, and A is the number of different types of final products. The categories used in the calculation of the copper in-use stocks in China are identified based on previous research,13,14 especially from studies for China.7 The calculation formula and the key parameters are presented in the SI (Table S6). On the basis of the calculated results of the copper stocks, the spatial distribution pattern is further characterized. Specifically, the top several subcategories accounting for the majority of the total in-use stocks are identified to represent the entire pattern of the whole reservoirs of copper in-use stocks. The spatial pattern is then measured in three ways, by the amount of copper stocks in each area (usually in a province), the amount of copper (kg) per km2 of land area, and the amount of copper (kg) on a per capita basis. The spatial allocation methods can be found in the SI (Table S7). 2.3. Uncertainty Evaluation. Both the top-down and the bottom-up approach employ data from different sources with varying data reliability. It is thus necessary to quantify the uncertainty of the input data and parameters.15 For the topdown model, the estimates of annual copper in-use stocks are based on the historical end-use flows of copper and assumptions of the lifetime distributions of these End uses (di). For the calculation of historical copper inflows to different end-use sectors, two parameters may contribute most to the inished uncertainty of the stocks estimates, i.e., Fnet−impo,f and sector t ined net−impo,semis split of Fint , while Fcons,ref and F are assumed to be t t comparatively well reported. Additionally, the assumption of the lifetime distributions for copper in each end-use also appears to be a key factor that has an impact on resulting in-use stocks calculations. Therefore, a sensitivity analysis is first carried out to investigate the effect of the parameters in three aspects on the results. Then, a Monte Carlo simulation is applied to simulate all possible combinations for different degrees of variability. As most of the data or parameters only have one individual value due to scarcity of information for China, parameters are provided with the uniform distribution by taking their data quality into account as the possibility of

Ftcons,ref ined

1950−1959 1960−1969 1970−1979 1980−1989 1990−1999 2000−2012

∑ (Ni ,t × mi ,t )

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upgraded several times since the foundation of the People’s Republic of China in 1949,17 which undisputedly brought the surge of copper stocks in this category. Within the subcategory of equipment, from the 1950s to the 1970s military equipment contributed the most to copper stocks because of the Chinese government’s war preparation strategy at that time.18 After that, copper used by the military decreased and other uses have grown. However, copper for civilian use was relatively rare until the middle of the 1990s due to the planned economy, as previously mentioned.16 The shares of copper stocks in two categories, transportation and buildings, have increased, especially since the 1980s. For the buildings category, copper is predominantly used in the plumbing and electrical wiring of buildings in China; thus, the growth of copper used in buildings possibly benefited from the promotion of urbanization, which brought about a sharp increase in buildings, as official statistics show.19,20 The increase of copper stocks in transportation can be attributed to the growth of automobiles and the development of civil transportation systems.19,20 By the top-down method, the copper in-use stocks in 2012 are calculated as 60 Mt, which means approximately 84% of the cumulative societal consumption of copper during 1952−2012 is retained in in-use stocks. This magnitude of the copper in-use stocks is more than twice the current basic reserve of copper ore in China (28.9 Mt in 2009),21 which suggests that after 60 years of extraction and use, the copper stocks in societal use have become the largest reservoir of copper in China. Compared with the primary resources underground, the secondary deposits of in-use stocks are clustered with higher quality. Both the quickly growing trend and the huge magnitude have significant implications on future recycling opportunities in the coming decades. Figure 1 also shows the historical pattern of copper stocks per capita from the topdown approach. The stocks increased from a negligible level of less than 1 kg/capita in 1952 to 44 kg/capita in 2012 and the current level is about one-fifth that of developed countries.12,13 3.2. Present Reservoirs of Copper In-Use Stocks in China. According to the bottom-up estimation, China had copper in-use stocks of 48 Mt in 2012. The largest reservoir is the infrastructure sector, which contains 28 Mt copper, accounting for 58% of the total stocks. The in-use stock estimates for copper in equipment, buildings, and transportation are 19% (9 Mt), 14% (7 Mt), and 9% (4 Mt) of the total stocks, respectively. Figure 2 displays the percentages by subcategory. The majority of the copper stocks reside in the subcategory of EPTD. This is most likely due to the expansion of the transmission and distribution facilities and the consequent copper use in wire and cable, especially since 2000. The relative long lifetime of the infrastructure, which is

appearance. Coefficients of variation (CVs) are set according to the qualitative assessment of their data qualities (cf. SI for more detail). For the bottom-up calculation, a large number of parameters are not obtained from the statistics but from the literature or observed assumptions, as SI Table S6 of shows. Therefore, nonstatistical parameters are also assumed to follow a uniform distribution by assigning different CVs (cf. SI for more detail). The Monte Carlo simulations are conducted to quantitatively test the propagation of input uncertainty into the final results.

3. RESULTS AND ANALYSIS 3.1. Patterns of Copper In-Use Stocks Growth from 1952 to 2012. According to the top-down method, the total amount of copper in-use stocks shows a significant increasing trend over the past 60 years (Figure 1). Since the year 2000, in

Figure 1. Copper in-use stocks of China from 1952 to 2012 (by the top-down approach).

particular, approximately exponential growth has occurred. This expansion may be attributed to two major reasons: (1) the opening of the copper consumption market in the middle of the 1990s. Before that period, copper consumption was limited under the planned Chinese economy system. One example is that the former Ministry of Goods and Materials forbade 205 types of domestic products to use copper in 1988;16 and (2) the accelerating urbanization process since the 2000s and the subsequent increasing investment on urban infrastructure. Statistics show that the urbanization rate increased from 30.8% in 1999 to 36.2% in 2000, and kept a continuous growth ever since with an annual growth rate of approximately 1%. Before 1999, the urbanization rate was enhanced slowly and increased from 12.5% in 1952 to 30.4% in 1998 (data on urbanization rates can be found in the SI). Regarding the structure of copper in-use stocks, the categories of infrastructure and equipment remain the two largest components, although there has been a growth in all end-use sectors during the past 60 years. Copper uses in infrastructure exceeded those of equipment as the largest contributor in the middle of the 1980s, when the economic reform and opening policy were adopted, stimulating urbanization and associated infrastructure. Copper uses in infrastructure mainly involve electric power generation facilities and the electric power transmission and distribution systems (EPTD). It is clear that the national power grid has been

Figure 2. Category distribution of copper in-use stocks in China in 2012 (by the bottom-up approach). C

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Considering that the characterized copper stocks represent only 78% of the total amount, the actual density level should be even higher. Beijing (BJ) and Tianjin (TJ) also have relatively higher spatial density, with a level of more than 0.04 Gg/km2. Regarding copper (kg) per capita, BJ has the largest per capita value, whereas most provinces in the western China have relatively lower levels than those in eastern regions. If scaled up to the total quantity of 48 Mt, the copper in-use stocks of different areas fall in a range of approximately 32−50 kg/capita. Further analysis gives more interesting results. For example, Sichuan (SC) has relatively higher total copper in-use stocks (ranking sixth among all 31 areas of China), but the levels of copper (kg) per km2 and per capita have lower rankings. The main reason for the large total amount is that this province has a high population density. For provinces such as Xizang (XZ), the total amount, the copper per capita, and the copper per km2 are all the lowest of China, which reflects the lower level of economic development and the lower population density in this area. On the whole, results show the secondary deposits of the copper in-use stocks are predominately spatially distributed in the eastern region, which is generally characterized by higher levels of economic development and higher population densities in China. In contrast, most of the primary copper resources are located in the central and the western regions of China, such as Jiangxi (JX), Yunnan (YN), and Xizang (XZ). This pattern disparity reflects the contradiction between undeveloped regions, where natural resources are exploited, and developed regions, where huge resources are consumed. Furthermore, from the perspective of future copper recycling, the observed spatial patterns indicate that the copper recovery and recycling systems will be most productive if they are located in urban areas of the eastern region, which are generally characterized by higher levels of economic development and higher population densities. On the other hand, as salaries in the less wealthy areas are probably lower, recovering and recycling might still be economical. This may indicate that different recycling strategies are needed for different regions. 3.4. Prospects of Copper In-Use Stocks. Prospects of copper in-use stocks by 2050 are extrapolated regarding the major identified subsystems by a bottom-up approach (the same list as used in Section 3.3, i.e., EPTD, HD, MV, and RB), which are expected to maintain significant shares in future though their specific values would change.22 Details about the data and assumptions for the extrapolation can be found in the Section 6 of the SI. Figure 4a demonstrates the total population (TP) will reach a peak of 1.45 billion in 2030 and exhibit a moderate decline after that.23 The quantity of urban population (UP) will also attain the maximum in 2030 as a combination result of accelerating urbanization and growing population (calculated according to ref 24). As a contrast, the rural population (RP) has shrunk continuously since 1995 with the urbanization process. Figure 4b and c then show the trends of quantity indicators measured by per capita volume for the four subsystems. Considering it is hard to find a proper index to represent the scale of EPTD, per capita copper stocks is directly used here. As most of power grid projects will be accomplished in the coming decade,22 per capita copper stocks of EPTD will peak in 2025 and then keep stable, with the maximum approaching the same level as that of a typical U.S. city, New Haven,6 and higher than that of Japan.25 For the HD subsystem, five categories of

estimated up to 80 years, can partly explain it as well. Household durables (HD), residential buildings (RB), and motor vehicles (MV) also have large copper stocks, and these four subcategories account for 78% of the total stocks. Additionally, both the industrial equipment (IE) and the nonresidential buildings (NRB) occupy 6% of the total stocks. On a per capita basis, the total amount of copper stocks in use in China is approximately 36 kg, subdivided into infrastructure (21 kg), equipment (7 kg), buildings (5 kg), and transportation (3 kg). Table 2 shows the details of per Table 2. Per Capita Copper In-Use Stocks of China by Category in 2012 (by the Bottom-Up Approach) category infrastructure

transportation

buildings

equipment

total stocks

water supply and distribution electric power generation electric power transmission and distribution telecommunications broadcast and television network rail lines and urban rail transit system streetscape and traffic lights total infrastructure motor vehicle aircraft civil transport vessels railroad vehicles total transportation residential buildings nonresidential buildings total buildings household durables commercial equipment industrial equipment total equipment

per capita stocks in use (kg) 0.9 0.9 17.5 0.7 0.0 0.1 0.4 20.5 3.1 0.0 0.1 0.0 3.2 3.2 1.9 5.1 4.3 0.1 2.1 6.5 36

capita copper for China. Compared to the estimate for Connecticut in the United States,13 a huge gap in the per capita values mostly exists in the buildings category, with a difference of approximately 40−70 kg between the two regions. This large gap can be explained by a lower electrification rate in China, and less usage of copper plumping and hardware fittings in Chinese buildings compared to the United States. The lower usage of copper hardware fittings in China may possibly be caused by consumption habits instead of economic levels. 3.3. Present Spatial Distribution of Copper In-Use Stocks in China. The spatial distribution of the four identified major subsystems are chosen to represent the pattern of whole reservoirs of copper in-use stocks in China, i.e., EPTD, HD, RB, and MV. The spatial allocation method can be found in Table S7 of the SI. Figure 3 shows the results of the spatial distribution. Concerning the total amount of stocks, the provinces of Guangdong (GD), Shandong (SD), Henan (HA), Jiangsu (JS), and Hebei (HE) are the top five among the total 31 provinces of China. Measured by the copper in-use stocks (kg) per km2, the eastern region has a higher spatial distribution density than the other regions of China, possibly attributing to its higher population density and wealth. Shanghai (SH), one of the wealthiest areas in China, has the highest spatial density of approximately 0.1 Gg/km 2 . D

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Figure 3. Spatial distribution of copper in-use stocks in China in 2012 (by the bottom-up approach).

household appliances (HAs) are chosen to illustrate the entire subsystem, namely, air conditioners (AC), personal computers (PC), refrigerators (R), color televisions (TV), and washing machines (WM). The number of these HAs per urban resident (UR)/rural resident (RR) is used to be the quantity indicator. All categories of HAs per UR/RR will have reached the saturation level by around 2030 according to a logistic function.26 The quantity of the third subsystem is measured by the number of MV per capita, which will keep a continuous and sharp growth until 2030, thereafter the trend begins to be flat.27,28 The saturation value of 2030 is lower than that of developed regions but approximately equal to the current level of Beijing.29 Considering traffic conditions of Beijing and the environment/energy issues China is now facing with, this level would be the superior limit of China. To be consistent with the calculation process, the numbers of urban household (UH) and rural household (RH) are used to represent the proxy values of RBs. The quantity of UH and RH shows a pattern similar to UP and RP, respectively.23,24 Combining copper intensity of these subsystems with Figure 4a, b, and c (further details of calculation and data sources are elaborated in the SI), the prospects of copper in-use stocks are further quantified, as displayed by Figure 4d. Predictions show that both per capita and total amount of copper stocks will depict a continuous increase in the following decade, then experience an inflection between 2025 and 2030, and thereafter, either enter a relatively stabilization phase or drop moderately. Overall, the per capita stocks are estimated to reach a maximum of 106 kg in 2034, approximately double the current level. In terms of the total magnitude, it will peak at 154 Mt around

2030 and then drop gradually due to the shrinking population by around 2050. Regarding the treatment of quantity/intensity indicators and the calculations (as elaborated in the SI), prospects discussed in Figure 4d probably reflect the upper bound of copper in-use stocks for China. The level of 105−106 kg/capita is about a half that of the developed countries.12−14,30,31 However, this analysis indicates that per capita stocks of China will not necessarily achieve the same high level as the developed countries as Gordon mentioned.31 3.5. Uncertainty of Data and Parameters. Comparing the top-down results of 2012 with the bottom-up calculation, it is found that the latter is 20% lower than the former. As different assumptions and data inputs are adopted in these two approaches, it is hard to state which number is more reliable. The following will interpret the uncertainty of both approaches. The sensitivity analysis shows that for the top-down approach, the annual stocks results are mainly sensitive to product lifetime assumptions of end-use sectors and trade inished , while the effect of variations on sector estimation Fnet−impo,f t split of Fint is negligible compared to that of the other two parameters (see SI for details of the sensitivity analysis). The Monte Carlo simulation shows the aggregated uncertainties of historical copper in-use stocks induced by these three variations are within a range of 11% (Figure 5). In the year 2012, for example, the result is a distribution of the density of different results over time, ranging from 57 to 68 Mt (see SI for the histogram of copper in-use stocks for 2012). In general, the top-down forecasting model is relatively robust against such changes. Particularly, the possible reason for the negligible effect of the sector split of Fint is that its effect depends on the E

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Figure 4. Prospects of copper in-use stocks in China.

system. The parameter of average copper content in urban households contributes 12% of the variation. These uncertainties come from possible simplifications of the calculation process. Future work needs to be carried out to reduce the uncertainty in these fields.

4. POLICY IMPLICATIONS China has been trying to recycle more and more copper from domestic end uses, and the pattern of copper in-use stocks has important implications for closing copper cycles in future. If future extraction of copper ore keeps the scale of 1.27 Mt concentrate per year,2 the currently identified reserves of 27.8 Mt would be depleted in the next two decades. On the other hand, the domestic old scrap in 2012 can be estimated as 0.8 Mt, according to the top-down calculation of in-use stocks (SI), more than half of the imported scrap in the same year.2 The number is expected to sharply increase in the future because a large part of the current in-use stocks, 48 Mt by the bottom-up method or 60 Mt by the top-down method in 2012, will be transformed to copper scrap in the coming two decades (notice the values of m in lifetime distributions of end-use sectors range between 15 and 30). Furthermore, considering the potential scrap from the incoming increase of in-use stocks until 2030, a gradual shift of copper resources use from imports to domestic recycling sources will be expected in the future, and China will finally enter a copper scrap age at that time. In terms of effective urban mining in the future, the Chinese government should pay sufficient attention to the recycling of copper scrap. An analysis of the present reservoirs of copper in-use stocks in China helps to identify the hotspot of recycling copper scrap,

Figure 5. Aggregated uncertainties of the top-down calculation of copper in-use stocks in China.

lifetime distribution of different sectors, while the mode (m) of different Weibull distributions (as shown in the SI) are relatively close to each other. For the bottom-up calculation, the Monte Carlo simulation shows that the value of copper in-use stocks lies in the range from 45 Mt (5% quartile) to 54 Mt (95% quartile) (cf. SI for more details). That is, approximately −6%−12% of the variations from the calculated result. The sensitivity analysis shows that 77% of the variation can be explained by the coefficient used in the calculation of copper stocks in the EPTD F

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i.e., EPTD, HD, MV, and RB. China has been making great efforts to regulate recycling activities over the past decade. A notable example is the release of the Circular Economy Promotion Law in 2008,32 which addresses “reducing, reusing and recycling activities” and enforces manufacturers’ extended producer responsibility. However, the list of mandatory takeback products has not yet been completed due to the lack of standards. In this perspective, the results of this research will provide scientific evidence for the formulation of product coverage. Products, such as HD and MV, which play important roles in copper recycling, should be included in the coverage of mandatory take-back products. Contrarily, the EPTD belongs to a series of state-owned enterprises under the State Grid Corporation of China. This means the recycling of cable and transformers in this system would be more effective. The results of the spatial distribution of copper in-use stocks in China identify the key regions for the recycling of copper scrap. Obviously, cities in the eastern region of China are characterized by “high copper density” and thus should be the focus of recycling in the near future. However, taking a longterm perspective, the level of copper in-use stocks in the eastern regions will gradually approach saturation, at which point provinces in the central and western regions deserve sufficient attention. Furthermore, understanding the distribution patterns of stocks helps to establish different recycling systems in different regions, such as designing different recycling systems for HD in urban and rural areas.

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ASSOCIATED CONTENT

S Supporting Information *

Details on the method of calculating stocks, input data, and the uncertainty analysis. The Supporting Information is available free of charge on the ACS Publications website at DOI: 10.1021/acs.est.5b00917.

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AUTHOR INFORMATION

Corresponding Author

*Phone and fax: +86-25-89680532; e-mail: [email protected]. cn. Notes

The authors declare no competing financial interest.

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ACKNOWLEDGMENTS The research is financially supported by the Natural Science Foundation of China (71203090 and 41222012) and the Collaborative Innovation Center for Regional Environmental Quality.

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REFERENCES

(1) The Compilation Committee of China Non-ferrous Metals Industry Yearbook. Historic Data Compilation of China Nonferrous metal industry from 1949−1991; China Press Corporation: Beijing, 1993. (2) China Non-ferrous Metals Industry Association. Yearbook of nonferrous metals industry of China; China Non-ferrous Metals Industry Association Press: Beijing, 2013. (3) Zhang, L.; Yang, J. M.; Cai, Z. J.; Yuan, Z. W. Analysis of copper flows in China from 1975 to 2010. Sci. Total Environ. 2014, 478, 80− 89. (4) Yuan, Z. W.; Bi, J.; Moriguichi, Y. Circular economy: A new development strategy in China. J. Ind. Ecol. 2006, 10 (1−2), 4−8. (5) McMillan, C. A.; Moore, M. R.; Keoleian, G. A.; Bulkley, J. W. Quantifying US aluminum in-use stocks and their relationship with economic output. Ecol. Econ. 2010, 69 (12), 2606−2613. G

DOI: 10.1021/acs.est.5b00917 Environ. Sci. Technol. XXXX, XXX, XXX−XXX

Article

Environmental Science & Technology (32) Circular Economy Law of the People’s Republic of China. Adopted at the 4th Meeting of the Standing Committee of the 11th National People’s Congress, 2008.

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DOI: 10.1021/acs.est.5b00917 Environ. Sci. Technol. XXXX, XXX, XXX−XXX