2. E-Waste Generation and Processing, and the Resultant Health Impacts
2.1. The Health Impacts of Lead
2.2. The Health Impacts of Dioxins and Furans
2.3. The Health Impacts of Particulate Matter (PM)
3. Estimating the Deaths Due to Informal Processing of E-waste in Guiyu
3.1. Excess Pollutant Levels (Excess Body Burdens) in Guiyu
3.2. Increases in Mortality Rates
3.3. Estimated Deaths Due to Informal E-waste Processing
4. The Social Cost of Informal Processing of E-Waste in Guiyu
4.1. The Value of a Statistical Life in Guiyu and the Social Cost of Increased Mortality
4.2. The Impact of E-waste Processing on Cognitive Functioning and Earnings
4.3. Social Cost and Sensitivity Analysis
5. Limitations and Conclusions
Conflicts of Interest
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One might expect adult BLLs to be lower than child BLLs because adults may become more resilient to exposure to lead. Conversely, adult BLLs could be higher than child BLLs because lead accumulates in an adult over time.
That is, RR = MR(A)/MR(B), where MR is the mortality rate, A is a place with relatively high pollution, and B is the base place.
Probability that a resident of a polluted area is more likely to die than a resident of the base place = (RR – 1) × 100, expressed as a percentage.
The probability a resident of Guiyu is more likely to die than an average resident of some other town in China (expressed as a percentage), denoted ΔP(DG), equals [(RR – 1) × 100] × BBR.
The mortality rate in Guiyu, denoted MRG, equals (1 + ΔP(DG)/100) * MRC, where MRC is the average mortality rate in China.
The increased mortality rate in Guiyu, denoted ΔMRG, equals MRG – MRC. Alternatively, and more directly, one may compute this increased mortality rate using the formula: ΔMRG = (RR – 1) × BBR × MRC.
Estimates of inflation in China come from: https://data.worldbank.org/indicator/FP.CPI.TOTL.ZG and our estimate of the Yuan to US dollar exchange rate in 2018 comes from https://data.oecd.org/conversion/purchasing-power-parities-ppp.htm.
The National Bureau of Statistics of China (2018) estimates of the annual per capita incomes for lower middle- to upper middle-income Chinese in 2018 are 14,130–35,264 Yuan. Guiyu is a rural area within Guangdong. NBSC estimates of annual per capita incomes in this region range between 16,107–33,688 Yuan in 2018. Taking the average of these four figures produces an annual per capita income estimate of 22,748 Yuan. This amount is consistent with Guo et al. (2014) and Huang et al. (2015) who surveyed Guiyu households. Guo et al. (2014) found 42% earned less than 30,000 Yuan annually on a per capita basis, while Huang et al.’s (2015) found 75% earned more than 12,000 Yuan. Adjusting our estimate of 22,748 Yuan for inflation and converting it to US dollars based on purchasing power parity gives an average disposable income equal to $6514 in 2018 US dollars.
|Presently Applied Technologies||Best Available Technologies|
|1||Functioning components (like cables, power supplies) are removed for reuse.||E-waste is manually separated into reusable components and end-of-life parts.|
|2||Deflection and focusing coils are removed by breaking off the neck glass.||The e-waste is processed in an enclosed mechanical system which segregates the materials using a four step process:|
|a. E-waste is shredded into small pieces of two inches in size.|
|b. The e-waste pieces are placed into a magnetic separator where ferrous metals are collected.|
|c. The non-ferrous metals pass through an eddy current separator where the materials are sorted.|
|d. The dust generated is collected in a bag filter and sent to the metallurgical refining unit for processing.|
|3||The monitor is manually dismantled to extract steel, aluminum, and copper parts.||The e-waste is now sorted into five types of materials: glass, plastics, aluminum, ferrous metals and non-ferrous metals.|
|a. Ferrous metals are sold directly to steel plants.|
|b. Aluminum is sold directly to smelters to be reused for commercial purposes.|
|c. Plastics are segregated into recyclable and non-recyclable fractions, and processed accordingly.|
|4||Cables and other components are incinerated in the open to recover copper.||Non-ferrous materials are recycled in a controlled manner (smelting, electronic refining).|
|5||CRT glass and residues are crushed and disposed in an uncontrolled manner, without regard for emissions.||Hazardous materials are removed from the glass and transferred to external recyclers for proper processing.|
|Pollutant||Low Estimate||Best Estimate||High Estimate||Source|
|Guiyu||4.4||15.30||32.67||Huo et al. (2007)|
|China||8.20||Qu et al. (1993)|
|8.98||Qu et al. (1988)|
|4||8.00||12||Chen et al. (2001)|
|Blood levels of lead for China||4||8.39||12|
|Excess Body Burden of Lead (μg/dL)||0.4||6.91||20.67|
|Dioxins and Furans (Body Burden)|
|Guiyu||5.26||Chan et al. (2007)|
|Guiyu||10.68||Ma et al. (2008) and Geeraerts et al. (2015)|
|China||1.05||Sun et al. (2010)|
|Body burden for China||1.15|
|Excess Body Burden of Dioxins and Furans (TEQ/BW)||4.11||9.53|
|Guiyu||103.50||Deng et al. (2006)|
|China||86.24||61.48||Pandey et al. (2000)|
|Excess Body Burden of Particulate Matter Exposure (μg/m3)||5.50||17.26||42.02|
|Pollutant||Initial Risk Ratio (RR)||Excess Body Burden in Reference Study||Excess Body Burden in Guiyu (from Table 2)||Excess Body Burden Ratio (BBR)||Increased Probability of Death in Guiyu||Guiyu Mortality Rate||Increased Mortality Rate in Guiyu||Excess Deaths in Guiyu||Social Cost of Excess Deaths ($ millions)|
|Dioxins and Furans||1.15||16.10||9.53||0.59||8.88||0.78||0.06||96||62|
|Particulate Matter (acute)||1.00||1.67||17.26||10.36||0.62||0.72||0.00||7||4|
|Particulate Matter (chronic)||1.05||16.67||17.26||1.04||5.18||0.76||0.04||56||36|
|Dioxins and Furans||1.00||16.10||9.53||0.59||-||0.72||0.00||-||0|
|Particulate Matter (acute)||1.00||1.67||17.26||10.36||0.52||0.72||0.00||6||4|
|Particulate Matter (chronic)||1.04||16.67||17.26||1.04||4.14||0.75||0.03||45||29|
|Dioxins and Furans||1.30||16.10||9.53||0.59||17.76||0.85||0.13||192||125|
|Particulate Matter (acute)||1.00||1.67||17.26||10.36||0.72||0.73||0.01||8||5|
|Particulate Matter (chronic)||1.06||16.67||17.26||1.04||6.21||0.76||0.04||67||44|
|Study||Yuan||USD||USD (2018)||Source or Notes|
|Willingness to Pay Studies:|
|Zhang (2002)||970,000||409,946||World Bank (2007)|
|Hammitt and Zhou (2006)||385,000||162,711||World Bank (2007)|
|Wang and Mullahy (2006)||775,000||322,944||World Bank (2007)|
|Wang and Mullahy (2006)||150,619||157,585||Hoffmann et al. (2017)|
|Krupnick et al. (2006)||1,400,000||547,392||World Bank (2007)|
|Hammitt and Zhou (2006)||10,500||15,826||Robinson et al. (2019)|
|Hammitt and Zhou (2006)||75,095||78,568||Hoffmann et al. (2017)|
|Guo et al. (2006)||24,000||32,750||Robinson et al. (2019)|
|Guo and Hammitt (2009)||184,366||268,890||Robinson et al. (2019)|
|Guo and Hammitt (2009)||107,789||112,774||Hoffmann et al. (2017)|
|Qin et al. (2013)||188,000||205,616||Robinson et al. (2019)|
|Liu and Zhao (2013)||117,186||122,606||Hoffmann et al. (2017)|
|Ohdoko et al. (2013)||1,464,412||1,532,139||Hoffmann et al. (2017)|
|Yang et al. (2016)||3,729,492||1,114,620||Hoffmann et al. (2017)|
|Wang and He (2014)||332,310||347,679||Hoffmann et al. (2017)|
|Hoffmann et al. (2017)||1,470,000||517,207|
|Benefit Transfer Studies:|
|Viscusi and Masterman (2017b)||1,364,000||1,444,325||E = 1.0.|
|The current authors||1,534,301||Updated GNI/capita; E = 1.0|
|The current authors||1,269,740||Updated GNI/capita; E = 1.1|
|The current authors||857,675||Lower Guiyu income; E=1.1|
|Based on Anqing, a relatively small city.|
|$ 400,000||$ 650,000||$ 900,000|
|E-waste processed: 1,000,000 tonnes|
|E-waste processed: 1,250,000 tonnes|
|E-waste processed: 1,500,000 tonnes|
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