Estimating the Evolution of Urban Mining Resources in Hong Kong, up

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Estimating the Evolution of Urban Mining Resources in Hong Kong, up to the Year 2050 Io Hou Kuong, Jinhui Li, Jian Zhang, and Xianlai Zeng Environ. Sci. Technol., Just Accepted Manuscript • DOI: 10.1021/acs.est.8b04063 • Publication Date (Web): 04 Jan 2019 Downloaded from http://pubs.acs.org on January 5, 2019

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Environmental Science & Technology

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Estimating the Evolution of Urban Mining Resources in

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Hong Kong, up to the Year 2050

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Io Hou Kuong,† Jinhui Li,† Jian Zhang,‡ and Xianlai Zeng*,†

4

†Key

5

Environment, Tsinghua University, Beijing 100084, China

6

‡Key

7

Management, Beijing Information Science and Technology University, Beijing 100192,

8

China

9

ABSTRACT

Laboratory for Solid Waste Management and Environment Safety, School of

Laboratory of Bid Data Decision Making for Green Development, School of Economic

10

Rapid urban metabolism is causing many resources flow from consumption to waste. But

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many of these wastes could be secondary resources, and cities could become urban mine—an

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increasing supply of future resources. Hong Kong, one of the most developed and populated

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cities in the world, has been demonstrated a completely metabolic evolution to be an urban

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mine, since the 1970s. Covering fourteen types of e-waste and eight types of end-of-life

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vehicles, this study first investigates Hong Kong’s evolution in urban mine. The potential

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output weight of urban mine quickly grew from 117 kt in 2000 to 368 kt in 2014 and is

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estimated to remain in the range of 300-350 kt over the years 2015-2050, with 40-50

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kg/cap/year. The economic potential of urban mining, for eighteen metals, plastic, glass, and

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rubber tires, will be approximately US$2 billion annually, mainly contributed by precious and 1 ACS Paragon Plus Environment


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rare metals. All the obtained results contribute to Hong Kong’s waste management and

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promise to have positive impact on urban mining and circular economy for the other

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less-developed cities or regions.

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Table of Contents (TOC)/Abstract Art

24 25 26

1. INTRODUCTION

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Technological advances have shaped our modern life and resulted in the transfer of many

28

mineral resources from underground to aboveground. Yet, as these resources diminish,

29

society is searching for alternatives to virgin mining. One such alternative is urban mining:

30

the recovery of raw materials from anthropogenic stock such as discarded products.1, 2 Two of

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the most dominant waste streams, worldwide, are waste electrical and electronic equipment

32

(WEEE or e-waste) and end-of-life vehicles (ELV).3,

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e-waste and ELV are challenging for waste management, they can also be considered critical

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sources of urban mining, since they contain significant amounts of recyclable resources,

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ranging from metals to plastic, along with other critical materials.5-8

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While the burgeoning volumes of

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Urban mining of e-waste and ELVs can bring both environmental and economic benefits.

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Recovering substances within the waste can not only prevent hazardous and persistent

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pollutants from polluting air, water and soil,9 but also alleviate resource burdens on extracting

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new materials10, 11. Economically, urban mining is becoming more cost-effective than virgin

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mining of natural ores,12 and can also exploit the more highly concentrated resources in urban

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mine.13, 14 However, some studies from other countries have shown that a large portion of rare

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metals is still not effectively recycled despite a high recycling rate of ELVs and e-waste.8, 15

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Hong Kong in the 1950s became the first of the four Asian Tiger economies to undergo rapid

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industrialization, driven by textile exports, manufacturing industries, and re-export of goods

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to the Chinese mainland. Currently, it is one of the most developed and populated cities in the

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world. Thus, Hong Kong has experienced an entire evolution of urban metabolism,16, 17 from a

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light manufacturing center in the 1970s to a financial center in the 1990s and a tourism-driven

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economy in the 2000s.18, 19 The number of licensed private cars in Hong Kong increased from

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93,000 in 1970 to 536,025 in 2016.20 Consequently, e-waste and ELVs have been two of the

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most significant problems in this densely populated area of 1106 km2. Furthermore, the actual

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volume of e-waste may be far higher than reported, since some burgeoning product streams

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such as mobile phones are not considered in the total.

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As the concept of a closed-loop sustainable economy is becoming more popular, many

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countries and regions have launched Producer Responsibility Schemes (PRSs) in order to 3 ACS Paragon Plus Environment


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collect and recover materials in e-waste and ELVs.21-26 The Hong Kong government certified

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PRS for eight categories of electric and electronic equipment (EEE) control in 2017, but there

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is still no PRS for ELV control. In order to support PRS enhancement, a study estimating the

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amount of e-waste and ELV and the distribution of their resources is necessary. However,

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there are no complete or reliable sales statistics for Hong Kong electric vehicles or EEE for

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researchers to use as a basis for accurate estimations. Hence prior studies were based mainly

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on assumptions, and did not accurately forecast the amount or market value of recyclable

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resources of the last few decades.26, 27 These facts have inspired further studies, to produce

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more reliable estimates of the amount of e-waste and ELV in order to investigate the urban

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mining potential in Hong Kong.

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The objectives of this study were: 1) to suggest general approaches for estimating e-waste and

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ELV generation under given data constraints; 2) to forecast annual e-waste and ELV

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generation up to the year 2050, and 3) to estimate the amount and market value of resources

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within urban mine in Hong Kong. Section 2 presents some data regarding e-waste and ELV

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production and resource distribution, and introduces the stock-based model, Weibull

70

distribution, and other approaches. Results are demonstrated and discussed in Section 3,

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including the generated output and economic potential of urban mining, uncertainty analysis,

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and comparisons between this study and previous studies conducted in several other countries

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and regions. Based on the results of this study, brief suggestions on PRS enhancement for 4 ACS Paragon Plus Environment

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stakeholders are presented in the last section.

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2. DATA AND METHODS

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2.1. Data. This study considered 14 categories of electrical and electronic equipment (EEE):

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air conditioners (AC); refrigerators (RF); washing machines (WM); single-machine

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telephones (TPh); range hoods (RH); electric water heaters (EWH); gas water heaters

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(GWH); cathode-ray tube television sets (TV-CRT); liquid-crystal television sets (TV-LCD);

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desktop computers (Desktop); laptop computers (Laptop); machines for printing, faxing, and

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copying

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(Mon-LCD); and mobile phones (MPh). Data were sourced by Hong Kong Harmonized

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System (HKHS) code (see Table S1 in supporting information (SI)); import and export

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quantities were obtained from the Hong Kong Census and Statistics Department. Other data

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were adapted from studies in Mainland China and the Netherlands.5,

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available data were needed in this study, specifically:

(PFCM);

cathode-ray-tube

monitors

(Mon-CRT);

liquid-crystal

28, 29

monitors

Almost all the

87



Sales statistics for EEE for the years 1993-2017 (SI Table S2) 30

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

Sales statistics for private vehicle for the years 1966-2016 (SI Figure S2)20, 31

89



Average weight and lifespan distribution of EEE (SI Table S3 and S4)

5, 28

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Distribution of valuable resources in e-waste and ELV, including base metals,

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precious and rare metals, and plastic (SI Tables S5 &S6), and

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

Market prices of the resources (SI Table S7). 5 ACS Paragon Plus Environment


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2.2. Methods. It was critical to use an appropriate model to estimate the amount of e-waste

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and ELV that would be generated, based on these data. Various models have been adopted for

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estimating the generation of e-waste and ELV within a particular boundary, including the

96

material flow analysis (MFA) method, the market supply method, the Stanford method, time

97

series models and stock-based models.23,

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availability as well as model reliability and robustness.29 As Yedla (2015)32 has argued, a

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combination of methods should be employed to produce an accurate estimation, when data

100

are limited. In this study, a time-series model and a stock-based method were fused with MFA

101

in order to estimate the volumes of e-waste and ELV generation.

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MFA is a method that tracks material flowing into and out of system boundaries, and was

103

employed here for estimating e-waste generation from EEE sales figures.25, 33, 34 The number

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of products purchased (sales data) can be calculated using the following equation.

105

28

The selection of a model is subject to data

C  DI E  I E

(1)

106

where C, D, I, and E denote sales, domestic production, import data, and export data,

107

respectively, for EEE. In the case of Hong Kong, domestic production can be ignored since

108

almost all this commodity is imported.

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Two methods are adopted for measuring future sales data. The first is used for products

110

whose sales amount is relatively stable, such as RFs and TPhs, whose markets can be

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considered saturated. The future sales number of products is assumed as the average amount 6 ACS Paragon Plus Environment

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of sales within the products’ expected lifespans. This method can be also adopted for products

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whose sales amount fluctuates greatly, such as RH. The second method is logistical model

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regression, which is used to model the ‘S-curve’ growth of population within the limitations

115

of the environment35. Because EEE ownership is limited by population, the logistical model

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has often been adopted by researchers to approximate EEE consumption.36, 37 Assuming that

117

products will not be replaced by innovations, and that sales rates will not decline, EEE

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ownership is assumed to be limited by saturation level.33 This method can be adopted for

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innovations like MPhs, laptops, and LCD products. The logistical model can be described by

120

the equation below,

121

y

ymax 1  ae

 b  x  t0 

(2)

122

Where y is the sales data of products in the year x; t0 is the first year in the period; ymax is the

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saturation amount; and a and b are two parameters describing the growth rate. Based on the

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historical data, we used logistical model regression to approximate future sales data of EEE

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and electric vehicles. The simulations were performed by Matlab.

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E-waste. E-waste generation is sensitive to sales data and product lifespan.38 The shorter the

127

product lifespan, the more frequently the product is replaced. EEE lifespan, which may vary

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not only between different categories of products but also for the same product, depends on

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the product’s expected lifespan and consumers’ use preferences.34 Weibull distribution,

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introduced by Oguchi et al. (2010)39 for estimating commodity lifespan, has demonstrated 7 ACS Paragon Plus Environment


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reliability because products rarely fail to work after a very short time of use, and are usually

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still working after a very long time of use. The peak of obsolescence rate can be seen at the

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expected lifespan of an EEE. The lifespan distribution of EEE can be defined as the Weibull

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probability distribution function (pdf):

  dk  f  k     

135

k    

 1

  k    exp      , k  0      0, k 0

(3)

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Here dk is the Weibull probability, which refers to the portion of any category of products

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with a lifespan of k years; the scale parameter  describes the expected lifespan of the

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product; while the shape parameter  describes how many units of the products are assumed

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to be discarded simultaneously. It is verified that the parameters can be adapted from studies

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taken in Mainland China and the Netherlands (see SI Table S4 and Text S1).28, 29, 40, 41

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Sales data are converted to weight according to the average weight of each category of EEE.

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E-waste in different years can be determined via a time-series model. Using the historical and

143

estimated sales data and the product lifespan of various EEE, domestic e-waste generation can

144

be calculated by the following equation: n

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D  x    f  x P  x  dx  0

n

  P  2000   f  x  2000   P  2001  f  x  2001  ...  P  x  1  f 1 i 1

i

i

i

i

i

(4)

i

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Where D(x) is the e-waste generation in the year x; n represents the number of EEE

147

categories; Pi (20yy) represents the sales data of product i in the year 20yy; and fi(x-20yy) 8 ACS Paragon Plus Environment

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represents the portion of that product i purchased in the year 20yy that was discarded in the

149

year x.

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ELV. Since reliable sales and possession statistics for vehicles in Hong Kong can be easily

151

obtained, the stock-based model is employed for estimating ELV generation. The approach is

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as follows:

Ft out  Ft in   St  St 1 

153

(5)

154

where Ftin and Ftout respectively represent the number of vehicles first used and wasted in the year

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t; and St and St denote vehicle stock in the years t and t-1 respectively.

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Vehicle stock is influenced mainly by various socioeconomic factors such as population and

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household income.42,

158

vehicle price. As private vehicles, including private passenger cars and motorcycles, are usually

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shared among household members, household income can be assumed as the main factor

160

influencing private vehicle sales and stock data. The number of public transport vehicles (i.e.

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public buses, public light buses, and taxis) are linked to the Hong Kong population, while the

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number of commercial and social-use vehicles (i.e. trucks, private buses, private light buses, and

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special & government vehicles) is subject to the overall economic development level, described by

164

GDP.

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An ‘S-curve’ growth rate can be substantially accurate for illustrating the relationship between

166

average private vehicle possession per household and household income.44-46 Models such as the

43

For example, private vehicle ownership mainly depends on income and



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logistical model or the Gompertz and Richards model have been suggested as possible simulations

168

for determining this relationship.47 Because fewer parameters have to be confirmed in the logistical

169

function, future sales and stock data were predicted using the logistical model, based on data for

170

household vehicle possession and household income for 1966-2016 (SI Figure S2A & S2B).

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Linking the historical private vehicle stock data from 1997 to 2016 with household income, the

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optimum saturation ownership for private cars and motorcycles per 1000 households were

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determined as 250 and 40, respectively. Household income was assumed to increase linearly.

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Based on the data for new vehicle registrations for the period 1997-2016, 17 units of PC and 1.8

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units of MC respectively would be purchased annually per 1000 households (SI Figure S2C). It is

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known that demand for public transport (e.g. buses, light buses, and taxis) increases directly as the

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population grows.43

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Urban Mining Potential. Determining material recycling priority is of vital importance for

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stakeholders to solve the e-waste and ELV problems.48 Taking into consideration the market value,

180

environmental impacts, and resource scarcity, some researchers suggested the concept of ‘metal

181

criticality’ or ‘recyclability’ to choose targeted materials for recycling.49-52 In order to attract

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producers to urban mining, this study focused solely on economic profit from recyclable resources

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contained in urban mine. The annual amount and market value of resources, which together

184

indicate potential economic profit, can be respectively determined by Eqs. 6 and 7:

185

n

S j   Fi  x   cij i 1


(6)

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I avg   p j  avg   S j

(7)

j

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where Sj is the amount of resource j within n categories of EEE and vehicles; i represents the ith

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category of EEE; Fi(x) is the cumulative weight of the ith category of EEE that is wasted in the

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year x; and cij represents the portion of resource j contained in ith category of EEE. Iavg is the total

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average market value of all resources, and Pj(avg) is the market value per unit of weight of resource j

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within a given period. Since the resource distributions in different types of ELVs are uncertain,

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average resource concentrations are used as an approximate, in terms of per vehicle. Another

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assumption is that the substance encased in the EEE and vehicles will not be emitted during in-use

194

phase because substance losses can be negligible compare to the initial content in the products.

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Sensitivity and Uncertainty Analysis. The main method for determining how results change in

196

response to input variations and evaluating result imprecision is to analyze sensitivity and

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uncertainty.53 Generally, sensitivity analysis is conducted by changing one parameter and

198

observing how the results are affected, while uncertainty analysis is conducted by investigating the

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range of possible data output.28 Herein the sensitivity of the e-waste generation to product lifespan

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is analyzed, along with the market value error of urban mining. Other parameters such as product

201

weight and resource content are examined for uncertainty analysis. The uncertainty of each

202

parameter is independently performed by a Monte Carlo simulation (105 iterations),54 and thereby

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the uncertainty analysis of urban mine output can be carried out.

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3. RESULTS AND DISCUSSION 11 ACS Paragon Plus Environment


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3.1. Urban Mine Output Volume. E-waste. Linear and logistic regressions were employed for the

206

historical sales data of EEEs for 2000-2016 (SI Figure S1) to uncover the future consumption up to

207

2050 (SI Table S2). Illegal exportation and transfer of e-waste to Hongkong are not considered

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here owing to lack of exact statistics55, 56. The e-waste derived from these EEEs is in the process of

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evolving from the weight to the structure. In 2000, the weight was only 29kt, but it reached 114kt

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in 2010 and peaked at 194kt in 2017 (Figure 1A). Since Hong Kong is a highly developed city,

211

new EEEs can quickly penetrate the market in Hong Kong. Thus, the peak duration of e-waste

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generation from the booming emergence of electronics can be expected to be much less than for

213

most developing countries. In the remaining years of 2020-2050, the annual e-waste generation

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can be expected to maintain a stable range of 175-190 kt (Figure 1A) as a result of saturation of

215

EEE, as is typically seen in developed countries such as Denmark, Sweden, and Norway.25

E-waste weight (kt)

200

216

RF MPh desktop Mon-CRT EWH

WM TPh TV-CRT Mon-LCD GWH

AC Laptop TV-LCD RH PFCM

150 100

(B) 30

50 0 2000

This study (2015)

25 Average of e-waste (kg/cap)

(A)

Netherlands

20

Sweden Macau

15 Hong Kong Mexico Russia

10 S.Africa

5

2020

2030

Year

2040

0

2050

Denmark

Switzerland

Finland

Brazil China India

0 2010

This study (2050)

20 40 60 GDP per capita (kUS$)

80

100

217

Figure 1. E-waste generation projections for 2000-2050: (A) weight; (B) comparison of averages among

218

different countries or regions. Note: The dotted line infers that the amount of e-waste is related to economic 12 ACS Paragon Plus Environment

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development, and the data source could be seen at SI Table S8.

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The landscape of e-waste categories has evolved since 2000 (Figure1A and SI Figure S5). In 2010,

221

TV-CRTs, RFs, and MPhs were the three main contributors, responsible for over 80% of e-waste;

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while in 2020, the main contributors had changed to ACs, laptops, TV-CRTs, and Mon-LCDs,

223

responsible for 60%; while by 2030, they are expected to become ACs, laptops, desktops, and

224

TV-LCDs, responsible for 74%. In addition, frequent replacements of TVs and monitors have a

225

critical impact on the e-waste generation structure.57 The waste share of TV-CRTs, to all discarded

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TVs, is estimated to drop from 56% in 2012 to only 8% in 2030, while that of Mon-CRTs will

227

drop from 10% in 2012 to below 0.1% in 2030 (SI Figure S6).

228

Economic growth can significantly elevate e-waste generation.58 Each Hong Kong resident

229

generates more e-waste than the residents of Macau or even some European countries (e.g., the

230

Netherlands, Sweden, Switzerland, Denmark, and Finland),25, 59-62 as shown as Figure 1B. Even

231

though generation per capita is expected to decline from 24.4 kg in 2020 to 23.0 kg in 2050, it will

232

still be far more than that of Switzerland, which is less than 15 kg annually. The e-waste

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generation in developing countries, including BRICS (Brazil, Russia, India, China, and South

234

Africa) and Mexico, is likely to match that in developed regions after industrialization.28, 63, 64

235

ELV. According to the data for the Hong Kong population and public transport from 1966 to 2016

236

(see SI Figure S3), the number of in-use vehicles remained steady, but the vehicle share per capita

237

was declining. Based on the increasing demand for public transportation, the number of public 13 ACS Paragon Plus Environment


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transports will be assumed proportional to population. The share per 1000 persons of public buses,

239

public light buses, and taxis will remain at 1.75, 0.6, and 2.5 respectively. Annual vehicle sales per

240

1000 persons were assumed to be 0.15, 0.07, and 0.25 respectively, by 2030.

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Vehicles for commercial and social use, including trucks, private buses, and private light buses,

242

along with special and governmental vehicles, are discussed in this section. The amount of these

243

vehicles should be related to economic development, which can be indicated by GDP. SI Figure S4

244

shows the relationship between vehicle share per 1000 persons and GDP, implying that saturation

245

has already been reached. Accounting for the slight rise in average possession per 1000 persons in

246

2016, we assumed the saturation shares of private buses, private light buses, government vehicles,

247

and trucks and special vehicles as 0.1, 1.25, 2.5, and 15.8 respectively. The sales share for every

248

1000 persons of these types of vehicles is estimated to be 0.01, 0.03, 0.1 and 1.14 units

249

respectively.

250

Around 2035, the total quantity of vehicles in Hong Kong will exceed 1 million units, and by 2050

251

it will be approximately 1.03 million units (See Figure 2A). The private car is the dominant vehicle

252

with a range portion of 64%-72%. Vehicle possession per 1000 persons will be 127 units (see

253

detail in SI Figures S7-S9), which is lower than London with 350 units and Tokyo with 245 units,

254

because of the higher reliance of Hong Kong residents on public transport—88% of all transport

255

modes, compared to the other two cities: 33% for London passengers and 72% for Tokyo

256

passengers, reducing the demand for passenger cars.65-67 14 ACS Paragon Plus Environment

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Government Light bus (public) Trucks Buses (public) Private Cars

(B)

60 40

Government

Special

Light bus

Trucks

Buses

Taxis

Private Cars

Motorcycles

250

80

20 0 2000

257

Special Light bus (private) Buses (private) Taxis Motorcycles

ELV weight (kt)

ELV amount (1000 units)

(A)

200 150 100 50

2010

2020

2030

2040

0 2000

2050

Year

2010

2020

Year

2030

2040

2050

258

Figure 2. ELV generation from 2000 to 2050: (A) amount; (B) weight

259

The ELV amount can also be estimated using the stock-based model, as shown in Figure 2A. The

260

distribution of ELV is highly correlated with the distribution of in-use vehicles.68, 69 Private cars

261

are responsible for 75% of the ELV volume, while trucks and motorcycles are responsible for most

262

of the rest. In total, more than 70 thousand units of electric vehicles will be discarded by 2050.

263

Urban Mine Output. Based on the calculated weight of urban mine output for the years 2000-2017,

264

the output for the years 2018-2050 is estimated. The booming consumption of EEE and electric

265

vehicles during 1990-2010 resulted in a peak of urban mine output equal to 368 kt in 2014 (Figure

266

3). Thereafter, it is estimated to mostly generate 300-350 kt annually until 2050. The contribution

267

ratio of ELV to e-waste is expected to remain approximately 6:4, as the saturation of both waste

268

streams has been verified.



(A)

400

E-waste

(B)

ELV

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E-waste

100%

ELV

80%

300 250

Share

Total weight (kt)

350

200 150

60% 40%

100 20%

50 0

0% 2000

2000 2010 2020 2030 2040 2050

269

Year

2010

2020

2030

Year

2040

2050

270

Figure 3. Estimated urban mine output in Hong Kong from 2000 to 2050: (A) weight; (B) share

271

3.2. Valuable Resources in Urban Mine. The valuable resources are classified as base metals

272

(e.g., copper, aluminum, zinc, iron and cobalt), precious metals (e.g., gold, silver, palladium and

273

rhodium), rare metals (e.g., neodymium, yttrium, europium, lanthanum, cerium, praseodymium

274

and dysprosium), and plastic.70 Both the total amount and the structure of resources in urban mine

275

underwent changes between 2000 and 2017, and are expected to undergo more between 2018 and

276

2050. The total weights of urban-mined base metals in 2020 and 2050 are about threefold and

277

twofold those in 2010, respectively, with iron being the predominant metal, which is expected to

278

climb from 40 kt in 2000 to 60 kt in 2050 (Figure 4A). Since 2015, the annual amounts of plastic,

279

copper, and aluminum from urban mine have reached saturation, at 40 kt, 20 kt, and 16 kt,

280

respectively. Regarding the precious and rare metals, most will experience a two- to three-fold

281

growth in weight extracted, over the years 2000-2050, although this will vary depending on the

282

metal. The annual amounts of some metals will skyrocket; for example, palladium is anticipated to 16 ACS Paragon Plus Environment

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increase 6-fold, and cobalt 10-fold. These increases can be attributed to the increasing demand for

284

innovative EEE containing more precious metals than the “outdated” models. (A)

(B)

Plastic

Cu

Al

Fe

Zn

Co

Resource stock (t)

Resource stock (t)

1.0E+3 1.0E+2

1.0E+0 2000

(C)

Pd

In

Rh

40.00 30.00 20.00 10.00

1.0E+1

285

Ag

50.00

1.0E+5 1.0E+4

Au

2010

Nd La

2020

2030

2040

Year

Y Pr

Eu Dy

2000

2050

2010

2020

2030

Year

2040

2050

Ce

100.00

Resource stock (t)

10.00 1.00 0.10 0.01

286

0.00 2000

2010

2020

2030

Year

2040

2050

287

Figure 4. Distribution of various resources in urban mine: (A) base metals and plastic; (B) precious

288

metals; (C) rare metals

289

From an economic perspective, the constantly rising amount of resources will enhance the

290

economic potential of urban mine. As shown in Figure 5A, the average market value of annually

291

generated urban mine output will increase from 1 billion US$ in 2012, to 1.5 billion US$ in 2030, 17 ACS Paragon Plus Environment


Page 18 of 35

292

and approximately 2 billion US$ in 2050. Of all the encased materials, precious and rare metals

293

account for most of the economic potential, as shown as Figure 5B; and indium, despite

294

contributing only a small amount of this share, will produce the highest percentage of economic

295

value, at 37-70%, followed by palladium (9-20%) and gold (9-13%). Therefore, precious and rare

296

metals, along with other resources including copper and plastic, are worthy of being considered

297

important recyclables from urban mining.71 Nevertheless, these calculated rates are likely to

298

change. As more rare metals are applied in automobiles, for example, neodymium and cobalt will

299

have a higher recycling potential in the future.72, 73 (B)

2.5 2 1.5 1

La Co Zn

Ce In Fe

Rh Pd Al

Plastic Ag Cu

80% 60% 40%

45

50 20

40

20

35

Year

20

20

30 20

25

20

2050

20

2040

15

Year

20

2030

20

2020

12

0%

20

300

Pr Y Nd

20%

0.5 0 2010

Dy Eu Au

100%

Market value share

Market value (billion US$)

(A)3

301

Figure 5. Market value of urban mine output: (A) total market value; (B) value distribution of various

302

resources. Note: (A) The red line is the estimate of total market value and the grey lines present its error of

303

41%. (B) Three main contributors to market value are indium (37-70%), palladium (8-20%) and gold

304

(9-13%).

305

Sensitivity and Uncertainty Analysis. Using uncertainty analysis, distributions of total urban mine 18 ACS Paragon Plus Environment

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306

output and the resource stock of copper; iron, indium, and palladium are illustrated in Figure 6.

307

The distributions of total urban mine output in 2020 and 2050 can cover the results shown in

308

Figure 3A at the maximum probability interval. Meanwhile, the stock of two significant base

309

metals and two of the most precious metals, as estimated in Figure 4, were also validated by Monte

310

Carlo simulations. By these means, the robustness and accuracy of the estimations above can be

311

appropriately verified.

312

313 314

Figure 6. Uncertainty analysis with 95% confidence intervals: total urban mine output influenced by

315

product weight: (A) in 2020; (B) in 2050; and the stock of various resources in 2050 affected by resource

316

content and product weight: (C) copper; (D) indium.

317

Since EEE lifespans adapted from other studies may slightly deviate from the actual situation in

318

Hong Kong, due especially to hibernation of small devices, sensitivity analysis on lifespan 19 ACS Paragon Plus Environment


319

variation has to be conducted and is illustrated in SI Figure S13. Influences are significant for short

320

time estimates. In estimating the e-waste generation in 2012, one-year lifespan extension could

321

reduce the waste generation by 12.5%, while one-year lifespan reduction could augment the waste

322

generation by 15.7%. However, no deviation more than 0.5% will be seen for the estimates after

323

2025. This validates the robustness of the urban mine output estimates, which will not be unduly

324

affected by EEE lifespan, for long-term estimates. SI Figure S13 (B) and (C) suggest that the

325

output of each category of EEE varies with lifespan. The more the lifespan variation is, the more

326

the waste generation is deviated. Nevertheless, regarding economic fluctuation, the market value

327

of urban mining shows an error of 41% (see Figure 5A). Therefore, it is crucial to consider

328

economic factors when evaluating urban mining potential.

329

Restraints and Limitations. Due to the nature of forecasting, the estimates can be substantially

330

affected by several limitations, which generally originate from data, model and contingency.

331

Uncertainties and errors of the original data may be caused by sample selection and data

332

collection. In this study, EEE and vehicle sales, stock and output data, which are derived from

333

import and export data, may omit local-manufactured, local-assembled and unconsumed products.

334

Another sensitive data source is the resource content in e-waste and ELV owing to its variability

335

driven by cost reduction and technologic advancement. Manufacturers tend to consume less metal

336

in the products, (i.e. metal thrifting) or achieve enhancement by changing material content. Both

337

will affect our results very significantly. Moreover, as more types of metals are being employed in 20 ACS Paragon Plus Environment

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EEE and vehicles, this study may not cover all the metals that are used in the future. From

339

perspective of modeling, parameter adaptations and model simplifications can lower the accuracy

340

of the estimates. Adapting parameters (e.g. product lifespan) from other studies may influence the

341

results since they are time-and space-dependent, and approximations (e.g. in ELV generation) are

342

able to readily simplify the estimations but, to some extent, with deviation. Some errors can be

343

caused by unpredictable situations in the future. For instance, technological advancement could

344

greatly reduce metal usage in EEE and thus degrade the urban mining potential.74 Nonetheless, the

345

estimations are still reliable and robust according to the sensitivity and uncertainty analysis.

346

4. IMPLICATIONS

347

This study offers stakeholders an overview of the urban mining potential in Hong Kong. The

348

generation amount of e-waste will fluctuate between 175kt and 200 kt, but each year only 70 kt75

349

are thrown away and only 56 kt76 are disposed of in an ALBA facility. Consequently, there will be

350

over 10 kt of e-waste stock in homes and offices, at any given time. Although a PRS on discarded

351

EEE has been launched in Hong Kong, reducing-ewaste-at-the-source policies for manufacturers

352

are still lacking. Soon, the Hong Kong government ought to enhance e-waste collection and

353

increase the number of certified recycling facilities.

354

Prospectively, Hong Kong’s urban mine resources annually range from 300 kt to 350 kt, with per

355

capita values of 40-50 kg per year. If all the resources encased in the 350kt of urban mine were

356

recovered, especially the precious and rare metals, the economic recycling potential would be US$ 21 ACS Paragon Plus Environment


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357

2 billion every year. Furthermore, this study may suggest that reclaiming metals from urban mine

358

is economically competitive. For instance, the total cost per 1kg reclaimed copper is US$1.70/kg,

359

which is only 23% of the market value of US$7.30/kg12. Therefore, to cope with emerging

360

resource shortages and the increasing demand for precious and rare metals, urban mining from

361

e-waste and ELV should be the main approach for recyclable materials in the future.77,

362

results obtained here can provide the Hong Kong government and product producers some

363

fundamental information about harvesting urban mine resources. The authors anticipate further

364

studies to enhance the methodology (e.g. by considering metal substitution and enhancing model

365

parameters) and covering more types of urban mine sources and recyclable resources. Lastly, since

366

Hong Kong as an experienced city has demonstrated the evolution of urban mine, other cities or

367

regions could anticipate the process of urban metabolism.

368

ASSOCIATED CONTENT

369

Supporting Information

370

The Supporting Information is available free of charge on the ACS Publications website at DOI:

371

**. Tables S1−S10; Text S1; and Figures S1−S14 (PDF)

372

AUTHOR INFORMATION

373

Corresponding Author

374

* Address: Rm 805, Sino-Italian Ecological Energy Efficient Building, Tsinghua University,

375

Beijing 100084, China; Tel: +86 10 6279 7163; Fax: +86 10 6277 2048; E-mail: 22 ACS Paragon Plus Environment

78

The

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[email protected] (X. Zeng)

377

ORCID

378

Jinhui Li: 0000-0001-7819-478X

379

Xianlai Zeng: 0000-0001-5563-6098

380

Notes

381

The authors declare no competing financial interest.

382

Author contributions. I. H. K. calculated the results and drafted the paper, J. L. and J. Z.

383

contributed their insights, and X. Z. designed the paper and supervised this research.

384

ACKNOWLEDGEMENTS. The work is financially supported by Asia Research Centre

385

in Tsinghua University (2018-B1), and Beijing Education Committee S&T Program,

386

KM201711232016, Modeling and System Development of Industrial Park Resource

387

Recycling Networks, 2017/01-2018/12. We appreciate the Transport Department and the

388

Census and Statistics Department of the Government of Hong Kong SAR for providing

389

original statistics for EEE and vehicles. Specially, we thank all colleagues of Prof. Jinhui

390

Li’s team for giving valuable suggestions. We also acknowledge Prof. Julie B.

391

Zimmerman and four anonymous reviewers for the valuable comments and suggestions.

392

ABBREVIATIONS

393

AC Air conditioner

394

CDF Cumulative distribution function 23 ACS Paragon Plus Environment


395

DPC Desktop personal computer

396

EEE Electrical and electronic equipment

397

ELV End-of-life vehicle

398

EoL End-of-life

399

EWH Electric water heater

400

FM Fax machine

401

GWH Gas water heater

402

HKHS Hong Kong Harmonized System

403

ICT Information and communications technology

404

LPC Laptop personal computer

405

MFA Material flow analysis

406

MPh Mobile phone

407

PDF Probability density function

408

PRS Producer Responsibility Schemes

409

RF Refrigerator

410

RH Range hood

411

TPh Telephone with single machine

412

TV Television

413

WEEE Waste electrical and electronic equipment 24 ACS Paragon Plus Environment

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WM washing machine

415

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