Reconciling China’s economic development with global and
local environmental protection
Mun Ho
(Harvard U. & Resources for the Future)
Dale Jorgenson
(Harvard University)
Mitigation of air pollution and climate Change in China Workshop
Oslo, October 17-19 2004
A research project of
Institute of Environmental Science & Engr, Tsinghua
University
Harvard University Center for the Environment
Hao Jiming, Wang Shuxiao, Liu Bingjiang, Lu Yongqi
Sue Greco, James Hammitt, Mun HO, Dale Jorgenson, Jon Levy,
Chris Nielsen, ZHOU Ying
http://www.ksg.harvard.edu/cbg/ptep/
http://environment.harvard.edu/activities/sponsored/chinaproject/
Topics
• Intake Fraction method to estimate health damages from
air pollution
• Contrast with World Bank (1997) Clear Water Blue Skies
method of Lvovsky et. al. (1997,2000)
• Estimate of damages by industry; by health effects; etc.
• Use of ‘green tax’ policies to reduce damages and promote
tax reform and economic efficiency
Methodology
Goal: Efficient national policies to reduce local health
damages, also calculate coincident CO2 reductions
Step 1: Estimate marginal damages by industry (in terms of
yuan of damages per yuan of output).
Step 2: Estimate marginal damages by type of fuel
Step 3: Construct an economic model that traces output and
inter-industry transactions, including fuel use; traces
consumption and investment. Use model to simulate
‘green tax’ policy effects.
Table 1 Sector emissions in 1997
Sector
1
2
7
8
11
12
13
14
15
17
23
25
26
32
Agriculture
Coal mining and processing
Food products, tobacco
Textile goods
Paper products, printing
Petroleum refining & coking
Chemical
Nonmetal mineral products
Metals smelting & pressing
Machinery and equipment
Electricity, steam, hot water
Construction
Transport & warehousing
Health, Educ., other services
Households
Total
TSP (kton)
Combustion
160
181
310
166
201
72
672
2549
632
125
3953
121
361
416
462
11369
Process
0
65
16
3
92
76
200
10756
1244
24
72
0
0
0
0
12974
SO2 (kton)
Combustion Process
367
163
467
332
254
112
1216
1525
739
185
7846
414
609
714
778
17452
0
29
11
2
8
51
214
654
925
4
49
0
0
0
0
2089
Table 2 Emissions, fuel use and output 1997
1
2
7
8
11
12
13
14
15
17
23
25
26
32
Sector
Agriculture
Coal mining and processing
Food products, tobacco
Textile goods
Paper products, printing
Petroleum refining & coking
Chemical
Nonmetal mineral products
Metals smelting & pressing
Machinery and equipment
Electricity, steam, hot water
Construction
Transport & warehousing
Health, educ., other services
Households
Totals
TSP
Gross
Coal
Oil
(combus)
Output
use
use
(kton)
(bil. yuan) (mil tons) (mil tons)
159.7
2467.7
12.7
9.61
180.7
238.0
40.8
2.72
310.4
1371.3
33.2
1.74
166.1
913.0
21.8
1.06
200.5
445.5
18.1
1.34
71.6
308.5
20.0
8.75
671.8
1488.8
148.5
32.08
10.34
2548.8
874.9
206.7
632.0
750.2
183.2
8.81
125.4
882.1
36.2
3.81
14.80
3953.0
380.8
384.9
120.8
1738.6
9.4
20.63
361.0
506.6
17.5
23.07
416.0
658.5
33.5
1.15
461.7
37.1
2.11
20067
11369.1
1350.3
189.8
Concentration in major cities in China
400
350
micro gram/m3
300
250
TSP
SO2
200
150
100
50
0
1988
1990
1992
1994
1996
1998
2000
2002
2004
GDP and energy use
3.5
3
2.5
2
GDP
Coal
Oil
1.5
1
0.5
0
1990
1992
1994
1996
1998
2000
2002
2004
THE PROBLEM
• Have one number for pollutant concentration which is
believed to be the determinant of health effects and other
environmental damage
• Have emissions for each industry
• Need to assign each industry’s contribution to the ambient
concentration of PM and SO2
• Not feasible to model each emission source
• Lvovsky et. al. (1997,2000) method
• Intake Fraction (iF) method
Lvovsky et. al. (1997,2000) innovation:
C x   x ,low EM x ,low   x ,med EM x ,med   x ,high EM x ,high
3 γ’s derived from air dispersion models
Each industry assigned to low, medium or high stack height
Strength: simple to implement, comprehensive
Weakness: x=TSP, EM(TSP) is only primary TSP,
ignoring secondary TSP;
not all Cx due to human activity;
not all Cx due to local emissions;
Intake fractions for Electricity, Iron&Steel, Cement,
Chemicals, Transportation
(Wang Shuxiao, Liu Bingjiang, Zhou Ying et.al. )
- fraction of emissions from a source that is eventually ingested
- samples of plants from 5 cities
- used short range air dispersion model (ISCLT) to calculate
concentration due to primary PM and SO2
- used Calpuff model to long range effects, secondary SO4,NO3
- used detailed population location data to estimate exposures
- scaled up to national intake fractions using national samples
n
iFix   ( POPj  Concijx  BR ) / EM ix
j 1
iF calculation in Jinan city: Population, concentration, pop*conc
Estimated intake fractions for China industries
iF (50km domain)
TSP
SO2
Coal mining
Food products
Chemical
Cement
Metals smelting
Electricity
Transportation
Transp (NO3/NOx)
Health,Educ,Service
3.28E-06
3.46E-06
3.75E-06
2.16E-06
7.72E-05
6.61E-06
3.99E-06
6.03E-06
2.38E-06
iF (whole domain)
Secondary
TSP
SO2
iF(SO4/SO2)
1.05E-05
1.66E-05
4.40E-06
1.05E-05
1.66E-05
4.40E-06
9.84E-06
1.98E-05
4.40E-06
1.04E-05
1.20E-05
4.40E-06
1.13E-05
1.81E-05
4.40E-06
4.32E-06
4.76E-06
4.40E-06
7.72E-05
1.66E-05
4.40E-06
3.10E-06
3.86E-05
1.66E-05
4.40E-06
Dose-response and valuations of health effects, base parameters
Health Effect
Due to PM
Mortality
Respiratory hospital admissions
Emergency room visits
Restricted activity days
LRI/child(astmatic)
Asthma attacks
Chronic bronchitis
Respiratory symptoms
Due to SO2
Mortality
Chest discomfort
Respir. systems/child
Dose-response
per million/ g/m3
Valuation
yuan 1997
1.95
12
235
57500
23
2608
61
183000
370000
1751
142
14
80
25
48000
3.7
1.95
10000
5.0
370000
6.2
6.2
Health damages due to outdoor air pollution, 1997
iF method
LH method
Base param High param Base param
Damage (mil yuan)
137000
349500
230800
(% of GDP)
(1.84%)
(4.69%)
(3.10%)
Due to PM
116700
- primary PM
56100
- secondary PM
60600
Due to SO2
20300
218400
12400
Marginal damages, total damages by industry
Food products
Chemical
cement
Metals smelting
Electricity
Construction
Transportation
Health,Educ,Service
Households
Value
MD
yuan/yuan mil. Yuan
3594
0.00262
9041
0.00607
17148
0.01960
6563
0.00875
35654
0.09362
4786
0.00275
13519
0.02668
9824
0.01492
8035
Share
%
2.6
6.6
12.5
4.8
26.0
3.5
9.9
7.2
5.9
Marginal damages from fuels, averaged over all industries
Coal
Oil
Gas
per unit fuel
93.84 yuan/ton
54.28 yuan/ton
0.08 yuan/1000m3
per yuan of fuel
0.5751 yuan/yuan
0.0249 yuan/yuan
0.0002 yuan/yuan
National policies to reduce local and global pollution
Efficient general policy is tax on emissions but not feasible
- analyze 2 policies:
i) tax on output proportional to health damage caused
ii) tax on fuels proportional to health damage caused
- model calculates both costs and benefits of pollution
control :
- estimate reduction in health damages
- estimate change in output of each sector, change
in Consumption, change in GDP growth
(some cost, e.g. pollution control equipment, not
explicitly considered)
Methodology for cost-benefit analysis
- estimate a base case growth path of the economy
where pollution taxes = 0
- estimate alternative case where tax(j) = MD(j);
new revenues used to cut other pre-existing taxes
- compare the time paths of both cases
40000
4
35000
3.5
30000
3
25000
2.5
20000
2
15000
1.5
10000
1
5000
0
1997
0.5
0
2002
2007
GDP
2012
TSP
2017
SO2
Energy
2022
Energy (Bil. tons coal equiv.)
GDP (Bil. Yuan); Emissions (ktons)
Figure 1: GDP, Energy and Emissions Projected in Base Case
Base case projection of the economy (30 year growth)
Urban population (million)
GDP (billion 1997 yuan)
Coal Use (million tons)
Oil Use (million tons)
Carbon Emissions (million tons)
Primary particulate emissions (m tons)
Combustion TSP emissions
Noncombustion TSP emissions
Low height sources
SO2 emissions (million tons)
NOx (transportation) (million tons)
Premature Deaths (1,000)
Health Damage/GDP
1997
368
7,630
1,390
210
900
24.23
11.59
12.64
2.04
19.98
3.24
97
1.9%
2006
432
15,000
2,090
440
1,460
17.00
8.63
8.37
2.24
25.76
6.26
137
2.4%
2026 growth rate
568
1.46%
33,500
5.06%
2,750
2.30%
960
5.16%
2,260
3.12%
15.69
-1.44%
8.54
-1.01%
7.16
-1.88%
3.05
1.35%
34.59
1.85%
11.60
4.34%
232
2.96%
3.6%
Effects of an output tax based on pollution damages
Variable
GDP
Consumption
Investment
Coal use
Carbon emissions
Primary particulate emissions
Combustion TSP
Noncombustion TSP
High height source (Electricity)
Low height sources
SO2 Emissions
NOx (transportation)
Premature Deaths
Value of Health Damages
Change in other tax rates
Pollution tax/Total tax revenue
Effect in
1st Year
-0.04%
-0.06%
0.06%
-3.95%
-3.41%
-3.08%
-4.25%
-2.01%
-9.21%
-1.84%
-4.56%
-3.80%
-3.21%
-3.53%
-8.5%
9.1%
Effect in
20th Year
+0.18%
-0.01%
+0.55%
-3.36%
-2.41%
-2.30%
-4.00%
-1.05%
-7.42%
-0.92%
-4.00%
-2.93%
-2.88%
-3.01%
-6.1%
6.0%
Effects of an output tax based on pollution damages
Sector
Agriculture
Coal mining
Crude petroleum mining
Food products, tobacco
Petroleum refining
Chemical
Nonmetal mineral prod
Metals smelting
Electricity, steam,
Construction
Transport & warehousing
Post & telecommunication
Commerce & Restaurants
Social services
Health, Educ., services
Price
0.00
0.19
-0.58
-0.40
-0.44
0.25
1.63
0.80
5.76
0.65
2.24
-0.11
-0.98
0.71
1.54
Quantity
0.06
-3.85
-0.83
0.48
-1.20
-0.86
-2.10
-1.68
-6.69
-0.28
-3.10
-0.26
0.57
-1.03
-1.05
Effects of a fuel tax based on damages
Variable
GDP
Consumption
Investment
Coal use
Carbon emissions
Primary particulate emissions
Combustion TSP
Noncombustion TSP
High height source (Elect.)
Low height sources
SO2 emissions
NOx (transportation)
Premature Deaths
Value of Health Damages
Change in other tax rates
Pollution tax/Total tax revenue
1st year
-0.04%
-0.06%
+0.07%
-13.2%
-10.7%
-6.0%
-11.8%
-0.6%
-12.4%
-11.0%
-10.2%
-1.0%
-10.7%
-10.7%
-2.8%
2.72%
20th year
+0.05%
-0.02%
+0.20%
-18.2%
-12.5%
-9.0%
-16.3%
-0.7%
-16.3%
-15.3%
-14.0%
-1.6%
-14.3%
-14.0%
-2.1%
1.99%
Effects of a fuel tax based on damages
Sector
Price
Quantity
Agriculture
Coal mining
Crude petroleum mining
Food products, tobacco
Petroleum refining
Chemical
Nonmetal mineral prod
Metals smelting
Electricity, steam,
Construction
Transport & warehousing
Post & telecommunication
Commerce & Restaurants
Social services
Health, Educ., services
0.08
14.11
-0.03
-0.05
1.08
0.34
0.77
0.49
2.39
0.29
0.15
0.07
-0.30
0.12
0.23
0.06
-13.25
-0.49
0.20
-1.28
-0.34
-0.57
-0.78
-2.69
-0.01
-0.21
-0.08
0.31
-0.09
-0.17
Figure 3: Change in PM Emissions and Concentrations
due to a pollution tax on fuels
0
-2
-4
Percentage Change
-6
-8
-10
-12
-14
-16
-18
-20
1997
2002
2007
2012
2017
Emissions
Concentration
Damages
2022
Conclusions
- Magnitude of control costs of well designed policies is
much smaller than the local benefits, even ignoring
global benefits.
- Local pollution reduction would very likely involve carbon
emission reduction.
-Green tax policies help tax reform goals
- The short run adjustment costs may be higher than estimated
here leading to less control efforts by China if unaided by
outside world. Hence, rich countries may play a very
important role in tipping the balance towards greater efforts
and sooner.
Reconciling China’s economic development with global
and local environmental protection
Mun Ho (Harvard U. & Resources for the Future)
ho@rff.org
Dale Jorgenson (Harvard University)
djorgenson@harvard.edu
Papers available at:
www.ksg.harvard.edu/cbg/ptep/
http://environment.harvard.edu/uce/
Economic activity, pollution and health: A general
equilibrium framework
1) Emissions = f( output, fuel use, control technology)
2) Concentration = f(Emissions, meteorology, geography)
3) Health effects = f(Concentration, population; DR)
4) Value of Damage = f( Health effects, valuation(income))
5) Costs of control = f( level of reduction, control tech, ...)
6) f(output, prices, income, consumption, investment) = 0
7) Costs of control versus Value of Damage reduction
Lvovsky-Hughes simple method for all sectors:




1) EM jxt  jxQI jt   f  jxf AF
x=PM10,SO2
f=coal, oil, gas
j = sector 1,2,...


jft 

EM : emissions
QI : sector output
AF : fuel input
2) EM cx   EM jx
jc
c=high, medium, low height
3) CxN   high EM high, x   med EM med , x   low EM low, x
C : concentration
LH Method, continued:
DR : dose-response




4) HEht   x  DRhx (CxtN  x )POPtu 
h=Mortality,CB,....
5) Damageht Vht HEht
HE : health effect
POP: population
V : valuation in ¥
Damages by sector.
Marginal damage per unit emissions in sector j:
MD jx  Vht DRhx j POPt u er
h
Marginal damage per yuan(1997) of output:

EM jx,tb 


MD   MD jx

QI j ,tb 
x 
O
j
Marginal total damage of sector j:
MTD jt  MDOjt QI jt
Sector j's share of total marginal damages:
MTD jt /  MTD jt
j
iF method to estimate sector damages:
Health effects due to sector j:
N

iF
xj EM xj
HE hjS    DR hx

BR
x 




Marginal total damages due to j:

iFxjN EM xj
MTD jt  Vht HE  Vht   DR hx

BR
h
h
x 
S
hj



