Urbanization in China and
population exposure
- out of the frying pan and into the fire?
Kristin Aunan (CICERO)
Large developing economies: The rural-urban divide, air pollution and climate
policy
May 7–8, 2013
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2
Urban air quality in China is improving
Annual PM10 concentration in 86 key cities 2001-2011
China Class II
standard
WHO guideline
3
PM10 in rural kitchen – raditional solid
fuels (> 500 µg/m3 PM10)
Indoor air pollution from solid fuel use
dwarfs ambient levels in cities
4
Incomplete combustion in traditional
cookstoves
• ~10% of the fuel carbon is
converted into ‘Products of
Incomplete Combustion’
(PIC) (instead of producing heat…)
• Hundreds of common PICs
are toxic and carcinogenic:
Fine particles, CO, PAH,
oxygenated organics,
chlorinated organics
• The smoke reaches peoples’
lungs: Intake fraction is high
5
Exposure is a function of time activity pattern and
concentration levels
PM2.5 (µg/m3)
Cooking with tradition fuels (also affects living rooms) makes the big
difference for the exposure, particularly for women but also for men
Data for women from Guizhou (summer) (Aunan et al., 2013)
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Dirty household fuels: A main
source of short-lived climate
forcers
• BC (black carbon – soot) the second most
important single component when it comes to
global warming, after CO2 (Bond et al. 2013)
• Household solid fuels is the second largest BC
source in the world (after open burning of
forest/crop land etc) - ¼ of global BC
emissions
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Switch to clean household fuels is the most important
mitigation option for black carbon abatement (UNEP, 2011)
53%
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Household fuels in China:
Nearly half of the households lack clean fuels
Data from China Census 2010
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Fuel distribution 2000 and 2010
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2000
2010
12
Household fuel transition 2000–2010
• 346 million more
people use clean
fuels
• 145 million fewer
coal users
• 110 million fewer
biomass users
55%
10%
80%
23%
13
Energy poverty disparity increasing?
-biomass share is stable in rural areas
64%
60%
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Impacts on exposure of migration
Guizhou
• Moving from rural
areas: Household Air
Pollution (’traditional
air pollution sources’)
Beijing
• Moving to urban
areas: Traffic and
industry (’modern air
pollution sources’)
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Migrant flows
(China Census 2010)
• 261 mill migrants
• 227 million settled in
urban areas
• 138 mill rural-urban
migrants
• 52% are men
• 67% are
intraprovincial
migrants
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Previous (2000) and current (2010) settlement of
migrants (Example shown for migrants to Guangdong)
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Migrants to Shanghai
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We assume fuel use profile in migrants is average
for settlement region both in 2000 and 2010
(figure shows 2010 distribution)
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Population weighted exposure given fuel and
settlement (µg/m3 PM2.5, S.D.)
Urban
Rural
North
South
North
South
Clean
142 (18)
84 (18)
82 (7)
55 (7)
Coal
174 (18)
137 (22)
206 (15)
286 (28)
Biomass
440 (77)
485 (132)
433 (52)
496 (84)
(From Mestl et al 2007)
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Disentangling the impact on
population exposure of migration
• Our data allows us to calculate population
weighted exposure (PWE) for the total
population and for any sub-population
(place i, fuel j):
PWEP =
1
P
∑ (P
i, j
• PWEi , j )
i, j
• By definition we know that population
exposure (PE) for any population is:
PEP = PWEP x P (unit µg/m3pop)
and
PEtot_P = PEmigrant_P + PEnon-migrant_P
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..thus the attribution of migration to reduced
population exposure over the decade is given by
the share of ∆PEmigrants of ∆PE tot pop
~60%
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Estimated PWE 2000 and 2010
52 (36-70)
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∆PWE for total population is 52
µg/m3 PM2.5 – decomposition:
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Estimates for 8 largest host provinces
PWE 2000
PWE 2010
∆PWE
Migrant pop
(mill)
Guangdong
309
117
192
36,8
Zhejiang
317
118
199
19,9
Jiangsu
315
160
155
18,2
Shandong
308
203
106
13,7
Shanghai
255
94
161
12,7
Sichuan
320
199
122
11,7
Fujian
309
120
189
11,1
Beijing
251
150
102
10,5
Migrants to:
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Uncertainties
• Are the migrants representative for the area
(N/S, urban/rural) from where they left and
for the area where they settle down?
– Education level above average
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..but primarily for intraprovincial
migrants
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Uncertainties cont.
• Are the migrants representative for the area
(N/S, urban/rural) from where they left and
for the area where they settle down?
– Education level above average
– Income level lower (?)
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Large differences between intraand interprovincial migrants
Reason for
migrating
Total
migrants
Intraprovincial
migrants
(67%)
Interprovincial
migrants
(33%)
Manual labour 45%
31%
75%
Education
11%
15%
4%
Dependents
of migrants
14%
17%
9%
Demolition of
old residence
or moving
9%
13%
<1%
Figures from Zai Liang 2012,
based on China Census data
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Uncertainties cont.
• Are the migrants representative for the area
(N/S, urban/rural) from where they left and
for the area where they settle down?
– Education level above average
– Income level lower (?)
– Age group more narrow
• Probably some changes in average indoor and
outdoor pollution levels 2000-2010
• Working environment exposure not included
• Assumed PM2.5/PM10 ratio
• No measurements for floating population –
except in Shanghai
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Clean fuel in Shanghai (exposure to
PM2.5 from indoor and outdoor
environments)
Urban
Suburban and rural
Lejnarova
thesis
Used here (Mestl et
al 07)
~80
84 (urban S)
~60-70
55 (rural S)
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Rural Guizhou (South) and Shanxi
(North) - exposure to PM2.5 from indoor and
outdoor environments (own estimates)
RURAL
Used here (Mestl et al 07)
Guizhou BIOMASS_No chimney
381(±29)
496(±84) (rural S)
Guizhou BIOMASS_Chimney
308(±40)
496(±84) (rural S)
162
55(±7) (rural S)
Guizhou ~CLEAN (gas+?)
Guizhou COAL_Chimney
(..180 – 600..) 286(±28) (rural S)
Shanxi COAL_Chimney
163
206(±15)(rural N)
Shanxi CLEAN (electr.)
106
82(±7)(rural N)
U 174
U 142
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Summary
• Rural energy transition is
happening, but still most rural
households do not have clean fuels
• The dose is the demon: Household
air pollution still a larger cause of
PM2.5 exposure and ill health
• Increasing rural-urban energy
poverty disparities
• Rural-urban migration 2000-2010
has reduced exposure considerably,
for the individual and on a
population basis.

‘Out of the frying pan.’
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