China National Renewable Energy Centre – Danish Energy Agency
Economic Evaluation
of Externalities from
air-pollution in the
Chinese Energy
Sector
Final Report October 2014
Niels Bisgaard Pedersen, DEA in cooperation with
Xie Xuxuan, CNREC
Yanxu Zhang, Harvard University
16-10-2014
16-10-2014 Version 5
Indhold
Executive Summary ........................................................................................................ 2
Introduction ..................................................................................................................... 4
Economic versus financial costs .................................................................................. 4
Externalities ................................................................................................................. 4
ExternE........................................................................................................................ 5
Methods for monetization of Health Impacts ................................................................ 7
Overview of analysis of the environmental costs of China – four studies ......................... 9
Methods ....................................................................................................................... 9
Results ........................................................................................................................ 9
Country wide studies of China ..................................................................................... 9
Case studies in China ................................................................................................ 10
The CNREC – DEA study ............................................................................................. 11
Methodology and Results .......................................................................................... 11
Meteorological and atmospheric modeling ................................................................. 12
Dose-Response Functions......................................................................................... 15
Calculation of mortality ........................................................................................... 15
Monetary Evaluation .................................................................................................. 16
Total Costs ............................................................................................................. 16
Marginal costs per ton of emissions ....................................................................... 17
ExternE assessment of the economic costs of CO2 emissions .............................. 18
Uncertainty analysis and future directions ..................................................................... 18
Annex 1 Report from Research Fellow Yanxu Zhang, Harvard University ..................... 20
Annex 2 Emission Data from CREAM – EDO ................................................................ 20
Annex 3 Presentation 16 October 2014 at CNREC ....................................................... 20
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Executive Summary
CNREC and DEA have jointly with Research Fellow of Harvard University Yanxu Zhang
undertaken a study of the cost of air-pollution from the Chinese energy sector. The study
investigates the saved economic costs from air-pollution by comparing two scenarios for
the future development in the period 2015 to 2050:


Reference scenario based on the current 5YP
Max Renewable Energy scenario as developed by CNREC with 56% of renewable
energy in 2050
Both scenarios for the development of the energy sector in China are developed in the
framework of the China Renewable Energy Analysis Model (CREAM) developed at
CNREC with assistance from the Danish Energy Agency. The emissions estimates are
for: CO2, SO2, NOx and VOC.
Based on the methodologies developed by the ExternE project the current study aims at
estimating the economic costs of air-pollution derived from the energy sector in China.
The dispersion of SO2, NOx and VOC emissions has subsequently been modelled in the
GEOS-Chem to estimate air-concentrations of ozone and PM2.5 at a provincial level in
China.
Compared with the reference scenario, the national mean of ozone and PM2.5
concentrations are predicted to be reduced by 2.4 ppbv and 1.5 μg/m3, respectively, by
adoption of the Renewable Energy scenario.
The total avoided death during 2015-2050 is estimated to 1,750,000 with a reduction in
associated economic loss of 2.9 trillion (10**12) RMB based on a Value of Statistical Life
of 1.68 million RMB.
More than 80% of the avoided number of death and the reduction in the associated
economic loss is predicted to happen during 2030-2050 with only 20% during 20152030. More than 87% of the avoided numbers of death is contributed by the decrease of
PM2.5 concentrations, while ozone contributes with the remaining 13%.
The marginal economic costs of SO2, NOx’s contribution to PM2.5 concentration has
been calculated reducing the emissions in the Maximum Renewable Energy scenario
with 10%, and the following average marginal economic costs has been estimated.
Economic benefits of unit pollutant emission reductions (RMB/ton) in 2010 price level.
Emission type
Economic Evaluation RMB/ton(2010 price)
SO2
4 757
NOx
21 383
VOC
2 699
CO2
130
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The table also shows that the economic benefits from reductions of CO2 emissions are
estimated to 21 USD/per ton1 in parallel with the Environmental Protection Agency of
US, based on an estimation of damage costs. This can be utilized until prices are fixed
at a Chinese nationwide Emission Trade System for CO2 envisaged in 2016.
The total economic benefits associated with the reduction of CO2-emissions are
estimated to 8.7 Trillion (10**12) RMB over the whole period. The accumulated benefits
from the reduced PM2.5 exposure and the CO2 damage costs are thus 11.6 Trillion
(10**12) RMB.
1
3
1 USD = 6.21 RMB
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Introduction
This memo presents a joint DEA – CNREC work, with assistance from Yanxu Zhang
from Harvard University, on economic externalities related to air-pollution from the
energy production and consumption sectors in China. In relation to the energy sector
model, CREAM established in CNREC there is a need to cover also the costs of related
to green-house-gasses and to the human health impacts in China.
CNREC has developed a modelling framework for the Chinese energy sector comprising
various sub-models that are used to produce a number of scenarios for deployment of
renewable energy in order to strengthen the Chinese energy sectors sustainability.
The political commitment for renewable energy in China has been increasing over the
last years because of raising concerns for the environmental aspects and a stronger
commitment to reducing carbon emissions and air pollution.
It is important to highlight the economic costs of the environmental impact of the energy
sector when evaluating the options for the future energy supply. Making the
environmental benefits from increased usage of renewable energy or improved energy
efficiency visible in the political decision process is important, because those benefits
may provide for an important contribution to the cover the project costs, and it can
leverage support for deployment of renewable energy.
Therefore it has been decided to supplement the already existing framework with an
environmental module and produce a report highlighting important aspects of the
methodology used by the Danish Energy Agency in terms of internalizing environmental
costs. This report presents the results of this study.
Economic versus financial costs
Economic assessments are an integrated part of the political decision background when
it comes to energy projects and energy policies. This is important because the political
decisions should be based on information about the ‘true’ costs’ for the society - social
costs – as opposed to private costs faced by consumers and enterprises.
Externalities
External effects arise if, due to the activities of one person or group of persons, an
impact on another group occurs that is not taken into account or compensated by the
first group. The impact has an influence on the utility or welfare of the second group. Air
pollution and the emissions of greenhouse gases has negative impacts on health and
contributes to climate changes, and are thus examples of an externalities.
In the quantification of externalities often indicators are used for instance in terms of risk
to be exposed to a specific events, for instance illness, expressed as an increase in the
number of event per 100 0000 inhabitants.
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The monetary quantification is made by a functional relation between indicator and
monetary value, which is often linear with fixed unit costs per indicator.
We will distinguish between environmental impacts from air pollution related to public
health aspects caused by SO2, SO4, PM, NOx, O3 etc. and Global Warming effects
caused emission of carbon into the atmosphere through CO2.
There are other external impacts related to the energy sector like direct impacts on the
natural and socioeconomic environment: degradation of land, degradation of buildings
pollution of the landscape, water pollution, resettlement of population and destruction of
it commercial opportunities, changes in accidents and energy security, which will not be
considered in the following, as most of these are related to the establishment of energy
production plants and our focus is the production side.
It should also be noted that macroeconomic impacts like employment and depletion of
non-renewable resources not are considered as externalities.
ExternE
In a European context the so-called ExternE project succeeded in defining a
methodology that has been generally accepted and used widely to quantify impacts and
the economic costs of externalities. It involved a number of European countries. The
basic objective of ExternE was to prepare a methodology to get a transparent and
quantitative framework for assessment of economic costs. This approach developed is
called the Impact Pathway Approach (IPA)
The principles and four steps behind IPA are shown in the following Table 1, illustrating
how the impact of air-pollution on human health is assessed.
Table 1: Steps in the Impact Pathway Approach
Methodologies
Source (specification of site and
technology for instance a power
plant)
Result
Emissions of CO2, SO2, SO4, NOx,
PM2.5, PM10 etc.
Dispersion in the air. Travelling
distance possibly chemical
reactions in the atmosphere
(atmospheric dispersion models)
Increase in pollutants concentration
at receptor sites (concentration of
substances in the air)
Dose-response function, exposureresponse or concentration-response
function
Impacts on human health in terms
of mortality and morbidity
Monetary evaluation
Economic Cost (loss of income,
costs for health system)
Source: ExternE
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The principal greenhouse gases, CO2, CH4 and N2O stay in the atmosphere long
enough to mix uniformly over the entire globe. No specific dispersion calculation is
needed for those gases. For most other air pollutants dispersion in the atmosphere is
significant both on the local and regional level; and dispersions models are necessary to
estimate ambient concentration.
It is also necessary to account the chemical reactions that leads to transformation of the
primary pollutants to secondary pollutants, for example creation of sulphates from SO2.
In terms of costs, health impacts are by far the largest contributors to societal damage.
However ExternE analyses a number of other for instance: degradation of buildings and
nature, which will be disregarded in this study.
It is generally believed among human health experts that air pollution aggravates
morbidity and leads to premature mortality. The reasons are less certain, but the most
important impacts come from the long term impacts of particles.
The general approach to estimation of the impact of harmful emissions on morbidity is
the relative risk of premature death in relation to ambient air concentrations found in
epidemiological studies. These are expressed as % change in end-points per ug/m3, or
similar, which is linked with background rates of health end-points in the target
population, expressed as new cases per year per 100 000 people, the population size
and the increment in pollution.
The exposure-response function is often used in epidemiological studies to relate air
pollution and adverse health effects. The selection of exposure-response functions is
guided by the goal of achieving a balance between comprehensiveness and scientific
defensibility. Selecting the exposure-response function is an essential part of the
investigation design, because it greatly affects the magnitude of the eventual valuation
results.
The incidence of morbidity or mortality among the population can be regarded as
random events. Most epidemiological studies linking air pollution with health effects use
a relative risk model based on Poisson regression. Though most of them show
statistically significant and relatively linear associations between PM concentrations and
health endpoints, a log-linear function is more plausible and recommended for use to
estimate health effects for cities with high PM concentrations.
Table 2 provides a simple overview on how important pollutants affect human health.
Table 2 Health impacts of important air-pollutants
Primary Pollutant
Secondary Pollutant
Particles
SO2
SO4
NOx
NOx
NOx+VOC
6
Sulphates
Nitrates
Ozone
Impacts (End-points)
Mortality
Cardio-pulmonary morbidity
Mortality
Cardio-pulmonary morbidity
Like particles
Morbidity (? Not verified)
Like particles
Mortality
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CO
PAH
Greenhouse Gases
Morbidity
Mortality
Morbidity
Cancer
None directly (Global
warming)
The dose/exposure-response functions, linking dose and quantified damage (response)
are derived from national health databases, for instance US Environmental Protection
Agency.
Methods for monetization of Health Impacts
Since health and life are irreplaceable and have no market price, indirect approaches
such as willingness-to-pay (WTP), contingent valuation (CV) approach, amended human
capital (AHC) and cost of illness (COI) are used for evaluation of the health impacts.
The willingness-to-pay (WTP) approach values reductions in the risk of death, it values
each risk reduction by what a person would pay to obtain it. For example, a person might
be willing to pay 200 Yuan to reduce his/her risk of dying by 1 in 100 000 during the
coming year. This is his/her value of risk reduction. By definition, the value of a statistical
life is the sum of individuals’ willingness to pay for small risk reductions that together add
up to one statistical life. If a reduction in air pollution reduces each persons’ risk of dying
by 1 in 10.000 it will save one statistical life in a population of 100 000. The amount that
the 10.000 would pay for the risk reduction is known as the value of a statistical life
(VSL/VOSL).
The basic process of finding the average WTP survey has been described as follows by
OECD.
‘ the survey finds and average WTP of USD 30 for a reduction in the risk of dying from
air pollution from 3 in 100 000 to 2 in 100 000. This means that each individual is willing
to pay 30 USD to have this 1 in 100 000 reduction in risk. In the example for every 100
000 people, one death would be prevented with this risk reduction. Summing up the
individual WTP values of USD 30 over 100 000 people gives the VSL value of USD
30*100 000 = 3 million USD in this case. It is important to emphasise that the VSL is not
the value of an identified person’s life, but rather an aggregation of individual values for
small changes in risk of death’ (OECD 2012).
The Contingent value (CV) method is a non-market evaluation method that is widely
used in environmental impact assessment and cost-benefit analysis to reveal the
individual preferences. It can effectively measure the WTP to pay for improving their own
and others’ safety or health often through normally through questionnaires or
experiments.
In the Human capital (HC) approach, individuals are considered as the basic unit of
human capital, providing products and services. This approach measures the loss of life
and health according to a general standard for assessing physical capital, usually
represented as wage or labour capital, The HC approach merely takes the expected
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income loss as the loss from premature death. This raises ethical concerns, since there
is an implicit assumption that value of life differentiate, with different income. This is why,
the amended human capital (AHC) was put forward, and this approach uses per capita
GDP to measure the value of statistical year of life. This way the human capital is
established from the perspective of the entire country, neglecting individual differences,
as it is reflected in the VSL concept.
The Cost of illness (COI) method directly estimates the minimum value of health damage
by calculating various disease related costs, including pharmaceutical, diagnostic,
treatment and hospitalization costs, and the loss of income or social loss of GDP due to
illness. COI is widely used to measure the cost of different diseases in various regions
with different levels of economic and social development.
The difference in the results due to the chosen method is emphasized in The World
Banks calculation from 2007. Here, the mean total health cost associated with ambient
air pollution in urban areas of China in 2003 is 157 billion Yuan if the AHC approach is
used and 520 billion if WTP estimates are used. Hence, use of WTP increases total
costs by a factor 3.3.
The methods adapted in ExternE for quantifying economic costs are listed below.
Non-market valuation i.e. that are not or cannot be traded on the market, which results in
welfare changes. The evaluations either come from observing behavior in the real world
or experiments (Revealed preferences) or by asking individual about their preferences
(Stated preferences or contingent valuation).
Direct techniques are using market prices and replacement costs. Market prices can be
used for estimation of costs of marketed goods, for impacts on crops.



Replacement or restoration costs are an assessment of the costs to restore the
original quantity or quality.
Abatement costs are estimate of the costs of rectifying the negative impacts, i.e.
reversing the situation.
Avoided costs are costs of avoiding the harmful impacts by for instance filters for
removing emissions.
The following Table 3 summarizes the observations of the ExternE project regarding
quantification and valuation.
Table 3 Valuation principles for air-pollution
Quantification
Valuation principles
Air Pollution
YES
Willingness to Pay (WTP)
Global Warming – CO2
YES, partially
Abatement costs, avoided
costs and market prices
In the case of China we do not have information about assessments of the economic
costs of emission, except for CO2-emisisons. The economic costs per ton and the
emissions by fuel are well known and well determined for CO2, there are some
complications in relation to emissions as SO2, SO4, NOx and PM2.5.
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These other emissions are more complicated because they are plant specific and they
are chemically active in the atmosphere and their impacts therefore depend on the
chemical composition of the atmosphere and meteorological conditions. It is therefore
necessary to estimate the primary as well as secondary emissions. So to estimate the
harmful impacts of emissions on human health, buildings and nature it is necessary to
model the atmospheric transformation of emissions. Further their harmful impacts are
local, and not global as for CO2.
Overview of analysis of the environmental costs of China – four studies
A number of studies have estimated the economic costs of air pollution in China. The
conclusions of four important studies are summarized below.
There has been made a number of different studies on the monetary cost of health
impacts due to air pollution in China. Studies have been made both for the entire country
as well as case studies, focusing on specific areas.
Methods
The design on this type of investigation is rather complicated, and the choice of
methodology, endpoints and emission types investigated, a big say in the results. There
is a tendency in most studies made for China, to choose PM10 as an indicator for
ambient air pollution. The health effect endpoints due to PM10 include both mortality and
morbidity.
Results
For simplicity, the studies valuating in RMB are converted into USD, according to
xe.com, April 2014. (1 US$ = 6.21285 CNY) These are set in brackets in table 1.
Country wide studies of China
For the four economic assessments shown in table 1, regarding the whole of China, it is
seen that the results range from US$ 29,178.7 million to US$ 25.26 billion (AHC) to US$
106.5 billion. Zhang et al (2007) sets the monetary cost the lowest i.e. US$ 29,178.7
million. It is important to notice that this study only covers 111 Chinese cities, although it
covers most large and medium-sized cities, it is not covering the entire country. There
can be a number of other different reasons why this number is so much lower than the
two others.
Hou et al (2012) used GIS software to interpolate the annual mean PM10 concentration
collected in 421 cities; this showed that, in 2009, the central and western part of northwestern China, Beijing and Shandong has a PM10 concentration of 100 µg/m3 or greater.
Meanwhile, northern Xinjiang and the western part of southwest China had a PM10
concentration of 50 µg/m3 or lower. In addition, population and GDP data were used to
produce a county-specific-population density map, and a GDP per area distribution map.
This showed that China has a population and GDP pattern that is inconsistent with
atmospheric particulates that are in a spatial distribution. For example, north-western
China is relatively high in PM10 concentrations, even with the presence of an
underdeveloped economy and a low population density. This show, that apparently
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particulate matter concentrations are not alone sufficient to determine the population’s
exposure level (Hou et al, 2012).
Zhang (2013) has investigated the economic loss during the heavy haze event in
January 2013. The study is based on an evaluation of the total direct cost on health and
transportation, reported in the media. The results showed a total economic loss on US$
3.7 billion, among which the east cities and Beijing-Tianjin-Hebei region of China
suffered the most. The cost due to increased outpatient and emergency accounts for
98% of the total direct economic cost of the haze event, as this is nearly twice as much
as the total cost of health impacts attributed to particulate air pollution in non-haze
events estimated previous literature.
Matus et al (2011) uses PM10 and ozone concentrations beyond background levels, to
estimate the air pollution-related health impacts on the Chinese economy. They adopted
their exposure- response functions from two ExternE studies; all functions take a linear
form and do not assume any threshold effects. For modelling, the fourth version of the
MIT Emissions Prediction Policy Analysis (EPPA) model was applied. The study
estimates a loss of welfare in 2005 was 69.0 billion US$.
Case studies in China
For the case studies, a great variation in the monetary valuation is again seen. Some
studies focus on a great spectrum of endpoints, while others focus only on a few or a
single endpoint. It is also worth noticing that the studies chosen range from the years
2001 to 2010 which could also have a certain importance in the results. The study that
valuated the cost of the health impacts the highest is Huang et al (2011), concerning
health risks of particulate air pollution in the Pearl River Delta (PRD). The study uses two
approaches; COI and CV finding the cost to be US$ 4.7 billion and AHC and COI were
the cost is US$ 2.5 billion. One of the reason for the cost to be this high is that the PRD
is a manufacturing centre, and has had an annual increase in GDP of 21.2% from 19782007. In 2006, PRD produced 10.16% of the total national GDP in an area 0.43% of the
total land.
In a study by Greenpeace (2012) they found that premature deaths caused by PM2.5 cost
US$ 993 million dollars using the WTP approach. The study covers the four major cities:
Shanghai, Guangzhou, Xi’an and Beijing and was made for 2010.
Kan & Chen (2003) has investigated the particulate air pollution in Shanghai, with
mortality and morbidity health endpoint finding the cost in 2001 to be US$ 625.40 million,
with the use of WTP and COI.
Hammitt and Zhou (2005) has conducted a CV study in three locations in China to value
adverse health effects associated with air pollution and to study regional differences in
WTP. Three endpoints were included: cold, chronic bronchitis and fatality. Finding the
results to be between US$ 3- US$ 6, US$ 500- US$ 1000, US$ 4000-US$ 17.000,
respectively.
The economic costs of particulate air pollution from road transportation have Gou et al
(2010) attempted to estimate for the years 2004-2008. The results show that the total
economic cost of health impacts due to air pollution contributed from transport in Beijing
during 2004-2008 was 272, 297, 310, 323, 298 million US$ (mean value), respectively.
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The economic costs of road transport accounted for 0.52, 0.57, 0.60, 0.62, and 0.58% of
annual Beijing GDP from 2004 to 2008.
‘The cost of air pollution – Health Impacts of Road Transport’ from OECD estimates the
impact of air pollution on health in a number of countries including China. Their estimate
of the VSL for China is 975 000 in 2010 price level, based on the OECD base value of
USD 3 million adjusted for differences in per capita GDP at PPP with and income
elasticity to the power of 0.8. The total mortality costs from air pollution in 2010 is then
calculated to USD 1 245 000 million. This is based on mortality figures from 2010 of 1
278 890 in 2010 taken from the Institute for Health Metrics and Evaluation: The Global
Burden of Disease (2013). The figures from 2010 mark an increase in the number of
deaths by 5% since 2005. This study underlines that ‘ambient PM pollution’ occupies a
higher ranking as a risk factor in East Asia, predominantly, China than anywhere else. It
counts for 12% of the death tolls in China compared to 6% in the world as whole.
As conclusion it is fair to say that the information available concerning the health impacts
of air-pollution is scattered, and the results are not consistent and unambiguous. Our
focus in this project was to investigate the direct impact of each scenario on CO2
emissions and the human health in China. The aim of this exercise is to calculate the
costs per ton of emission for the most important emission types.
The CNREC – DEA study
The result of the study undertaken is briefly described below. Annex 1 provides the full
report from Yanxu Zhang.
The estimation of emissions in two of the scenarios – the reference scenario and the
maximum renewable energy – is provided by the CREAM (China Renewable Energy
Analysis Model) modelling framework at CNREC. It has been an important goal for the
project to demonstrate the methodology of the calculation and how the results can be
used in the CREAM EDO model. This project is only taking a part of the
Methodology and Results
The Chinese national emissions of SO2, NOx and NMVOC in the two scenarios has
been calculated in CREAM CGE (Competitive General Equilibrium) model. The emission
inventory includes for the two scenarios includes the years 2015, 2020, 2030, 2040 and
2050, as summarized in Table 1.
The emissions has been allocated geographically by representative emission specifying
emissions of anthropogenic ozone and aerosols precursors (NOx, CO, CH4, VOCs, BC,
OC, NH3, SO2), as well as the long-lived greenhouse gases. These inventories include
emissions from surface transport, shipping, aviation, energy production and distribution,
industrial combustion, residential and commercial fuel use, solvent use, waste
management and disposal, biomass burning (grass and forest files), agriculture (e.g.
fertilizer NOx and NH3), and agricultural waste burning.
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Table 1. Projected Chinese emissions for GEOS-Chem (unit: million tons / year)
Year
SO2
NOx
NMVOC
a
b
REF
RE
REF
RE
REF
RE
2015
18.7
17.9
28.5
28.1
9.38
8.45
2020
17.4
15.0
41.0
39.6
9.36
7.81
2030
16.4
12.2
15.3
12.1
8.62
5.52
2040
13.1
8.15
13.1
9.06
7.11
3.86
2050
9.87
4.39
10.7
5.91
6.50
2.74
a
b
Reference scenario; Renewable energy scenario.
Meteorological and atmospheric modeling
The air quality over China during 2015-2050 is simulated with a state-of-the-art
atmospheric chemistry and transport model (GEOS-Chem) and made by Research
Fellow Yanxu Zhang at Harvard University of Boston.
The GEOS-Chem model with present-day meteorological data (reference year 2004)
and future emissions as described above for the year 2015, 2020, 2030, 2040 and 2050.
As an example, Figure 1-5 show the spatial distribution of modeled annual mean
concentrations of ozone, PM2.5, SO2 and NOx at ground level over China in 2050. The
provincial mean of the concentrations of ozone and PM2.5 in these years are tabulated
in Annex 1.
50oN
50oN
50oN
45 N
45 N
45oN
40oN
40oN
40oN
o
o
o
35 N
35 N
35oN
30oN
30oN
30oN
o
o
o
25 N
25oN
25 N
20oN
20oN
80oE
0
90oE
15
100oE
30
110oE
120oE
45
130oE
20oN
80oE
60
0 ppbv
90oE
15
100oE
30
110oE
120oE
45
130oE
80oE
60
ppbv
0.00
90oE
1.50
100oE
3.00
110oE
120oE
130oE
4.50
Figure 1. Predicted ground ozone concentrations (ppbv) in 2050: left) reference
scenario; middle) high renewable energy scenario; right) the difference between these
two scenarios.
12
6.00
ppbv
16-10-2014 Version 5
50oN
50oN
50oN
45oN
45oN
45oN
o
o
40 N
40 N
40oN
35oN
35oN
35oN
30oN
o
30oN
o
25oN
30 N
o
25 N
25 N
20oN
20oN
80oE
0
90oE
25
100oE
110oE
50
120oE
75
20oN
80oE
130oE
0ug/m3
100
90oE
100oE
25
110oE
50
120oE
130oE
75
80oE
100
ug/m3
0.00
90oE
2.50
100oE
110oE
5.00
120oE
130oE
7.50
10.00
ug/m3
Figure 2. Same as Figure 1, but for PM2.5 (μg m-3).
50oN
o
45 N
o
50oN
50oN
45oN
45oN
o
40 N
40 N
40oN
35oN
35oN
35oN
o
30 N
30 N
30oN
25oN
25oN
25oN
o
o
20oN
o
20 N
20 N
80oE
0
90oE
6
100oE
110oE
12
120oE
130oE
18
o
80 E
25
0 ug/m3
o
90 E
6
o
100 E
o
110 E
12
o
120 E
80oE
o
130 E
18
0.00ug/m3
25
90oE
2.50
100oE
110oE
5.00
120oE
7.50
130oE
10.00
ug/m3
Figure 3. Same as Figure 1, but for non-dust PM2.5 (μg m-3)
50oN
50oN
45 N
45oN
40oN
40oN
o
35 N
35oN
30oN
30oN
o
25oN
o
25 N
20oN
20oN
80oE
0.00
90oE
0.08
100oE
110oE
0.15
120oE
80oE
130oE
0.23
0.00
ppbv
0.30
90oE
0.08
100oE
110oE
0.15
120oE
130oE
0.23
0.30
ppbv
Figure 4. Same as Figure 1, but for SO2(ppbv).
50oN
50oN
50oN
o
o
45 N
45 N
45oN
40oN
40oN
40oN
o
o
35 N
35 N
35oN
30oN
30oN
30oN
o
25 N
25 N
20oN
20oN
20oN
80oE
0.00
25oN
o
90oE
2.50
100oE
5.00
110oE
120oE
7.50
130oE
10.00
80oE
ppbv
0.00
90oE
2.50
100oE
110oE
5.00
Figure 5. Same as Figure 1, but for NOx(ppbv).
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120oE
7.50
130oE
10.00
80oE
0.00ppbv
90oE
1.00
100oE
2.00
110oE
120oE
3.00
130oE
4.00
ppbv
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The modeled ozone concentrations are the highest over northwest China where the
elevation is highest and more influenced by stratosphere sources (Figure 1 left). The
average concentrations can achieve levels of 40-50 ppbv in provinces such as Xinjiang,
Ningxia, Gansu and Xizang. The ozone concentrations are generally lower over more
populous east China, with levels generally lower than 40 ppbv (Table 2). Compared with
reference scenario (REF), the maximum renewable energy scenario (RE) causes 2-3
ppbv lower national mean ozone concentrations in 2050. The spatial pattern of the
difference between these two scenarios resembles that of anthropogenic emissions, with
large reductions in ozone concentrations of up to 6 ppbv over southeast China. The
reductions in ozone concentrations in northwest China is quite small (< 2 ppbv) despite
of the high natural background emissions.
The spatial distribution of PM2.5 concentrations is quite different from that of ozone and
is highest over west Inner Mongolia and south Xinjiang, where the dust emissions are
the highest (Figure 2). The annual mean PM2.5 concentrations over these regions can
be higher than 100 μg/m3, which is even higher than those measured in urban regions.
The dust fraction is not influenced by the anthropogenic emissions, and the spatial
pattern of reduced PM2.5 concentrations between the REF and RE scenarios is
determined by the non-dust fraction of PM2.5 concentrations as shown in Figure 3.
Overall, high renewable energy scenario reduces PM2.5 mainly over Henan (5.9 μg/m3),
Anhui (5.3 μg/m3) and Hunan (4.2 μg/m3) provinces, and 1.5 μg/m3 for the national
mean.
Unlike ozone and PM2.5, SO2 and NOx have much smaller influence from natural
sources. The spatial patterns of these pollutants as well as the difference between
scenarios more follow those of their corresponding anthropogenic emissions (Figure 4
and 5). Overall, high renewable energy scenario reduces the national mean NOx
concentrations for 0.68 ppbv. Interestingly, the lower ozone concentrations under the RE
scenario prolongs the lifetime of SO2 in the atmosphere, which compensates the effect
of anthropogenic emission reduction for SO2. As a result, the RE scenario is only 0.0008
ppbv lower than the REF scenario for the national mean concentrations for SO2.
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Dose-Response Functions
Calculation of mortality
With the calculated annual mean concentrations for various pollutants under the REF
and RE scenarios, we calculate the associated avoided death (ΔMort) by applying the
following health impact function (Anenberg et al. 2010):
ΔMort= y0(1-e-βΔC)Pop
where y0 is the baseline mortality rate, β is the concentration-response factor, ΔC is the
concentration difference of pollutants between RE and REF scenarios, and Pop is the
exposed population. β is derived from relative risks (RR) estimated in long-term
epidemiological studies assuming log-linear relationships between pollutant
concentrations and RR (Silva et al., 2013).
No certain association has been built up for environmental level NOx, as indicated in
Table X, and mortality by epidemiology studies, we exclude NOx in our further health
impact analysis. We also exclude SO2 because of the much smaller human health
effects compared to ozone and PM2.5. We are only looking at the contribution of SO2
and NOx for the concentration of Ozone and PM2.5
For ozone a concentration-response factor of 0.52% (0.27%-0.77% as 95% confidence
interval) increase in mortality per 10 ppbv increase of ozone (Bell et al., 2004) has been
adopted.
For PM2.5, we differentiate the associated death risk into long-term and short-term
following Huang and Zhang (2013). Although reliable health impact studies have been
conducted over the United States, such as the Harvard Six Cities adult cohort study and
American Cancer Society study (Dockery et al., 1993; Pope et al., 1995), they are not
directly applicable for Chinese population because of the much higher PM2.5
concentrations than in the US. Instead, we suggest adopting the mean of concentrationresponse factor for the short-term effects by four studies over China: 0.35% (0.05%-0.65%
as 95% confidence interval) per 10 μg/m3increase (Huang et al., 2012; Kan et al., 2007;
unpublished data from Peking University and South China Institute of Science). We use
a value of 2.96% (0.76%-5.04% as 95% confidence interval) per 10 μg/m3increase for
the long-term effect based on a meta study by Kan and Chen (2002).
The population and mortality data of each province in China in 2012 is obtained from the
National Bureau of Statistics of China (http://www.stats.gov.cn/).
Figure X illustrates the national total avoided death by adopting the high renewable
energy pathway over 2015-2050. The avoided death is quite small (4,000-5,000 per
year) during 2015-2020 because of the smaller difference in the anthropogenic
emissions between the RE and REF scenarios. The avoided death number becomes
more significant since 2020, and is over 50,000 per year in 2030 and achieves a level of
more than 90,000 per year in 2050 (Figure 6). The total avoided death during 2015-2050
is calculated as 1,750,000, with a reduction in associated economic loss of 2.9 trillion
RMB. More than 87% of this avoided death is contributed by the decrease of PM2.5
concentrations, while ozone contributes the remaining 13%. This is generally because of
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the larger concentration-response factor for PM2.5 than ozone, as well as its larger
sensitivity to anthropogenic emission reductions.
Table 4 tabulates the avoid death in each province during the period of 20152050 associated with exposure to ozone and PM2.5. The avoided death count during
2015-2050 is the largest over Shandong (199,000), Henan (196,000), Jiangsu (160,000),
Anhui (124,000), Hunan (110,000) and Hubei (108,000), because of their large exposed
population and significant air quality improvement. On the other hand, Hainan (227),
Macau (244) and Xizang (936) have the smallest avoided death count because of the
small populations.
100000
90000
Avoided death per year
80000
70000
PM2.5
Ozone
60000
50000
40000
30000
20000
10000
0
2015
2020
2030
2040
2050
Figure 6. Avoided death per year by adopting high renewable energy pathway over
China during 2015-2050.
Table 4. Avoided death per year associated with exposure to ozone and PM2.5 during
2015-2050 by adoption renewable energy scenario
Ozone
PM2.5
Year
2015 2020 2030 2040 2050 2015 2020 2030 2040 2050
China
633
378 5361 8759 13765 3501 5203 45383 63285 79547
The number of death allocated on province by year can be found in the Annex 1.
Monetary Evaluation
Total Costs
The economic loss associated with the death caused by air pollution is assessed by
Value Statistical Life (VSL), which is defined as the marginal cost of death prevention in
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a certain class of circumstances. We adopted a VSL of 1.68 million RMB (2010 price
level) following Huang and Zhang (2013).
The total avoided death during 2015-2050 is calculated as 1,750,000, with a reduction in
associated economic loss of 2.9 trillion RMB.
Marginal costs per ton of emissions
SO2, NOx and VOC and their contribution to PM2.5 concentration in air.
Because of the non-linear nature of the atmospheric chemistry reaction system, the
response of pollutant concentrations is often not proportional to the magnitude of
emission reductions. The sensitivity2 of pollutant concentrations to their precursor
emissions is largely dependent on the chemistry regime the state of atmosphere belongs
to. The concentrations of pollutants could increase even if emissions are reduced if
certain conditions are met (e.g. Madronich 2014). Therefore, the sensitivities of pollutant
concentrations to the emissions of their precursors for different years need to be
calculated separately. This is beyond the scope of this project to do that calculation.
As an alternative, a decrease in the emissions of SO2, NOx and VOC under the RE
scenario by 10% has been made, and the sensitivity has been calculated by dividing the
change of ozone and PM2.5 concentrations by the corresponding change of emissions.
Although the health risks associated with direct NOx and SO2 exposure are excluded in
this study as noted in section 2.1, the NOx and SO2 are important precursors of ozone
and PM2.5. The SO2 emissions contribute to the sulfate aerosol, which is an important
component of PM2.5 in China. Similarly, NOx contributes to both the nitrate aerosol
(another important component of PM2.5) and ozone. Therefore, the SO2 and NOx
emissions are still included in our cost analysis.
Table 5 lists the reduction of economic loss associated with per unit pollutant emission
reductions in 2015, 2020, 2030, 2040 and 2050. The economic benefits are 4,800-6,900,
3,300-36,000, and 2,000-3,400 RMB per ton emission reductions for SO2, NOx and
VOC, respectively. The sensitivity to NOx emission has larger variability than those of
SO2 and VOC largely because NOx emissions are important for the ozone and nitrate
chemistry.
Table 5. Economic benefits of unit pollutant emission reductions
(RMB/ton)
2015
2020
2030
2040
2050 Average
4757
SO2
5027
6425
4835
5816
6905
21383
NOx
6501
3298
28739
32395
35892
2699
VOC
3155
3443
2459
2354
2084
2
Defined as the ratio of the change of concentrations to the change of emissions.
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ExternE assessment of the economic costs of CO2 emissions
With regards to CO2 an assessment of the costs for achieving of the costs of Kyoto
targets in the EU can be interpreted as the collective WTP for early action against global
warming. For assessing technologies and fuel cycles in the mid-long term, the best
estimate is between 5 and 20 EUR/CO2eq with the higher range reflecting if emission
costs are controlled inside Europe. In ExternE a value of 19 EUR/tonCO2eq was
selected based on WTP studies in Switzerland. This value is also near the estimated
damage costs of CO2 produced by Environment Protection Agency of the US.
In China seven 7 pilot projects for Emission Trade System has been launched. The total
traded volumes of 1.7 million ton CO2 equivalent with the ETS in Hubei being by far the
largest (1.6 Million ton CO2 equivalents). The prices vary between USD 20 per ton CO2
in Shenzhen and USD 3.6 per ton CO2. The liquidity in the pilot projects is still low and
there is no secondary market. China is planning to launch a national ETS in 2015-16 as
one of the instruments to reduce carbon intensity and reduce emissions. The price of
carbon emissions in China is far above the current price in EU which is generally seen
as the most mature market worldwide.
For the time being, we will introduce a price of 20 USD per ton CO2 in the economic
analysis of CREAM-CGE, based on the estimates from EPA.
Uncertainty analysis and future directions
The uncertainty associated with our assessment is mainly from the concentrationresponse factors of ozone and PM2.5. Varying the concentration-response factors
causes large uncertainty for out estimate for the total avoided death: 1,750,000
(492,000-2,920,000 as 95% confidence intervals) and the reduction in associated
economic loss: 2.9 (0.83-4.9) trillion RMB during 2015-2050. To reduce this uncertainty,
long-term cohort study for the association between air pollution and its health effect for
Chinese population under relatively higher exposure concentrations is in urge need.
Another source of uncertainty relies on the spatial allocation of the emission inventory,
because it influences the spatial distribution of their concentrations. We assume the
spatial distribution of anthropogenic emissionis the same with the RCP 2.6 emission
inventory, which is not necessarily the case because the RCP inventory bears totally
different assumptions for different emission sources (van Vuuren et al., 2007).
Furthermore, we also assume the same spatial pattern for emissions under the REF and
RE scenarios. This misses the emission pattern changes driven by the enforcement of
renewable energy related policies. Developing future emission inventories with higher
spatial resolution is thus identified as another priority for future study.
The GEOS-Chem simulations are conducted under the present-day climate and
meteorological conditions without considering the climate change factors. According to
Wang et al. (2013), the ozone sensitivity to domestic emissions is slightly larger over
east China but lower over west China under future climate. This implies more stringent
emission reduction is required over the eastern China to meet a given ozone air quality
target if considering the compensating effect of climate change. Indeed, the climate
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change factors, as well as the feedback between air pollution and climate change, are
also needed to take into consideration in the future studies.
Further the emission data from CREAM model are national and not allocated by
provinces. It will be an important to address this problem in the further development of
CREAM model complex to accommodate estimation of emissions at provincial level.
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Annex 1 Report from Research Fellow Yanxu Zhang, Harvard University
Annex 2 Emission Data from CREAM – EDO
Annex 3 Presentation 16 October 2014 at CNREC
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