ABSTRACT
Particulate matter (PM) and tropospheric ozone (O3) influence air quality and cause adverse impacts on human health (Anenberg et al�, 2009; Liu et al�, 2009; Schwartz et al�, 2008; Levy et al�, 2001; Dockery et al�, 1993)� Vehicles are one of the major sources that contribute to emissions of PM and O3 precursors� China has been experiencing high concentrations of these species, and one of the main reasons is its exponential increase in the number of vehicles� Substantial air pollutant emissions in China come from the road transport sector (Zhang et al�, 2009; Ohara et al�, 2007)�
The number of total vehicles (cars, buses, and trucks) in China increased from 1�78 million in 1980 to 62�8 million in 2009� The number of gasoline vehicles expected in 2020 is 22 times more than that in 2000 (Saikawa et al�, 2011)� This explosive growth has led to substantial degradation of air quality, especially in urban areas (Cai and Xie, 2007)� As a measure to reduce vehicle emissions and enhance air quality, the Chinese central government implemented the first European vehicle emission standards (Euro standards) in 2001, and the standards have been tightened since then (Saikawa, 2013)� China nationally implemented the Euro 3 vehicle emission standards in 2008, and some cities such as Beijing and Shanghai have already implemented even more stringent Euro 4 emission standards (Saikawa and Urpelainen, 2014)� Although there are further plans to tighten emission standards nationally as well, it is of great interest to quantify the possible impact of China’s perfect implementation of the Euro 3 standards for all vehicles (except motorcycles and rural vehicles) by 2020� This is because the actual implementation of these standards will be the key for reducing air pollution in China and within the broader Asian region�
Previous studies have calculated the impact of China’s overall air quality� Johnson et al� (1997) estimated the costs of air pollution to be 4�6% of China’s GDP (gross domestic product) in 1995� The World Bank and China’s State Environmental Protection Administration (2007) together estimated that 1�3% of China’s GDP is lost due to air pollution in 2003 in a conservative estimate� Using the willingness-topay (WTP) measures, the value increased to 3�8% of China’s GDP� Matus et al� (2011) found 5% welfare losses from air pollution-related economic damage in China in 2005� Shindell et al� (2011) estimated the reductions in premature mortalities due to the implementation of the Euro 6 standards in China compared with the current standards to be more than 100,000 deaths in China� These values are significantly larger than the values estimated for the United States (Selin et al� 2009), indicating the magnitude of the problem�
Johnson et al� (1997) and the World Bank study (2007) have quantified health impacts due to PM less than a diameter of 10 µm or less (PM10) exposure only, and these impacts were calculated based on the assumptions of atmospheric concentrations within China due to the lack of data� Selin et al� (2009), Matus et al� (2011), and Shindell et al� (2011) have used global chemical transport model results to estimate exposure, but the grid resolution was rather coarse, with 4˚ latitude by 5˚ longitude for the former two, and 2˚ latitude by 2�5˚ longitude for the latter, possibly unable to capture the finer nonlinear O3 formation mechanism� Here, we quantify the health benefits from the reduced surface O3 and PM smaller than a diameter of 2�5 µm or less (PM2�5)
and the welfare gains in 2020 in China and Japan, due only to China’s implementation of the Euro 3 vehicle emission standards compared with no regulations within China�
We estimate atmospheric O3 and PM2�5 at a fine 40 km horizontal resolution using Weather Research and Forecasting coupled with Chemistry (WRF/Chem) for the domain that includes China and Japan (see Figure 8�1 for the model domain)� We do simulations for the two scenarios: (1) implementing no vehicle emissions regulation in China (NoPol) and (2) perfect implementation of the Euro 3 vehicle emission standards in China (Euro 3) (Saikawa et al�, 2011)� We use a general equilibrium (GE) MIT Emissions Prediction and Policy Analysis model with health effects (EPPA-HE) module to assess the increase in social welfare both in China and Japan due to the implementation of vehicle emission standards in China by 2020�
Using emissions for the two scenarios-(1) where we assume no vehicle emission standards in China from 2000 to 2020 (NoPol) and (2) where we assume all vehicles except motorcycles and rural vehicles gradually meet the Euro 3 emission standards by 2020 (Euro 3)—we estimate the atmospheric surface O3 and PM2�5 in China and Japan in 2020 with WRF/Chem, as described in Saikawa et al� (2011)� We then calculate population-weighted O3 and PM2�5 for China and Japan, and use these as inputs to a GE model to quantify health impacts and economic costs related to the
FIGURE 8.1 WRF/Chem model domain�
exposure to these species� We also conduct uncertainty analysis based on the confidence intervals of the concentration-response functions to estimate the range in the health effects estimate and economic calculations�
8.2.2.1 Model Description We use the regional on-line chemical transport model WRF/Chem (Grell et al� 2005 and references therein)� WRF/Chem is a state-of-the-art model where the transport and chemistry is calculated at the same time step� It has been used widely, used for modeling the United States, Mexico, and Asia (e�g�, Grell et al�, 2005; Ying et al�, 2009; Lin et al�, 2010; Wang et al�, 2010; Saikawa et al� 2011)� We use the horizontal resolution of 40 km × 40 km in this study with 31 vertical levels from the surface to 50 mb� The fine resolution of the model has an advantage to resolve local O3 formation better than the coarse resolution global models�
8.2.2.2 Emissions For all areas in the domain where the Regional Emissions in Asia (REAS) is available, we use the REAS emissions policy failure case (PFC) scenario for 2020� Where the REAS emissions do not exist, we use the A2 scenario in the Intergovernmental Panel on Climate Change (IPCC) Special Report on Emissions Scenarios (SRES), which assumes the constant economic growth as is the case in PFC� For the road transport sector in China, we use the modified emissions from the REAS inventory to create two 2020 simulations as explained in Saikawa et al� (2011)� In the NoPol scenario, we assume that emission factors for carbon monoxide (CO), nitrogen oxides (NOx), nonmethane volatile organic compound (NMVOC), black carbon (BC), and organic carbon (OC) stay the same as those in 2000 before the implementation of the Euro 1 (the least stringent) vehicle emission standards� In the Euro 3 scenario, we assume that all vehicles meet the Euro 3 standards in 2020, except motorcycles and rural vehicles, as the former is not currently regulated and because it is unclear how well the latter will be regulated� The emissions for the two scenarios are the same except for China’s road transport sector� Detailed explanations of the emissions are explained in Saikawa et al� (2011)�
8.2.2.3 Model Simulation We run WRF/Chem for 4 months-January, April, July, and October-to capture seasonality while minimizing the computer use� For each 1-month run, we spin up for 2 weeks� These runs give us the monthly average for each scenario, from which we create the annual mean O3 mole fractions and PM2�5 concentrations by taking the average of these 4 months� One simulation is done for 2000 (baseline) and two simulations are done for 2020-NoPol and Euro 3� These two 2020 scenarios allow us to quantify the health impacts due to surface O3 and PM2�5 changes only by China’s implementation of the Euro 3 vehicle standards, compared with no regulations� As an input to the GE model described below, we calculate population-weighted concentrations� We first quantify annual average O3 mole fractions and PM2�5 concentrations for China and Japan in 2000 and 2020 using our WRF/Chem model results� Next, we
calculate the population-weighted mixing ratio/concentration for O3/PM2�5 in 2000, using the gridded population distribution estimated at the Center for International Earth Science Information Network (CIESIN, 2005) for 2000� For quantifying the 2020 value, we use the 2015 CIESIN population grid and multiply it by the population change expected in 2020 relative to 2015, using the United Nations population estimate (United Nations, 2010)�
8.2.3.1 General Description We use the MIT EPPA-HE model (Paltsev et al� 2005; Matus et al� 2008; Nam et al� 2010)� EPPA is a multiregion, multisector computable general equilibrium (CGE) model, but we use it here to quantify the impacts in China and Japan only� EPPA-HE has been used for several policy analyses to quantify the costs and air pollution in different regions (Matus et al� 2008; Nam et al� 2010; Selin et al� 2009)�
The model uses the population-weighted concentrations in 16 regions (other than China and Japan, there are 14 regions that we do not analyze and thus stay as constant in the model) as inputs, and calculates cases of health-related diseases and premature mortalities as well as costs to the economy due to lost labor, services and leisure time� When there are health incidents, resources need to be devoted to health care and that prevents the money to be used for the rest of the economy� Labor and leisure time lost due to illness or death are valued at prevailing wage rates� Matus et al� (2008) describes further details, including the economic assumptions of the EPPA-HE model�
8.2.3.2 Concentration-Response Relationship Table 8�1 describes the concentration-response functions used in the EPPA-HE model to quantify the adverse health effects from the atmospheric O3 mole fractions and PM2�5 concentrations; 95% confidence intervals associated with these relations as found in epidemiology studies are also listed� We assume that the concentration-response relationship is the same in China and Japan, but we realize that this may not be correct� Later, we conduct a sensitivity analysis to account for the uncertainty that might be induced by this assumption as well as the relationship themselves� All the concentration-response relationships are assumed to be linear without a threshold, as suggested in studies for O3 (e�g� Bell et al�, 2006) and for PM2�5 (e�g� Schwartz et al�, 2008)� We calculate the reduced number of morbidity and mortality from acute exposure due to China’s implementation of the Euro 3 emission standards for each species and for Japan and China, separately by the following equation:
ΔCasesijk = CR ijk × ΔC jk × Pk
where ΔCasesijk , CR ijk, ΔCjk, Pk indicate the difference in the number of cases for health effect i and pollutant j between the two scenarios (NoPol and Euro 3) in
TABLE 8.1 Concentration-Response Functions for O3 (Unit of cases year-1 person-1 ppb-1) and PM2.5 (Unit of cases year-1 person-1 µg m-3), and Adverse Health Costs for Japan and China (USD 2000)
TABLE 8.1 (Continued ) Concentration-Response Functions for O3 (Unit of cases year-1 person-1 ppb-1) and PM2.5 (Unit of cases year-1 person-1 µg m-3), and Adverse Health Costs for Japan and China (USD 2000)
a Units are % increase in annual mortality year-1 person-1 ppb-1 for O3 and annual mortality year-1 person-1 ppb-1 for PM2�5�
country k; concentration-response relationship for health effect i and pollutant j in country k; the difference in the air pollutant concentration between the two scenarios (NoPol and Euro 3) for pollutant j; and affected population group in country k, respectively�
We calculate acute mortality in a similar manner with the following equation, using the baseline mortality rate, which we take from the World Bank Global Burden of Disease Study (Lopez et al�, 2006):
ΔCasesktAM = (CR jktAM j ∑ ×ΔCjkt × MktAll × Pkt )
where ΔCasesktAM, CR jktAM, ΔCjkt, M ktAll, and Pkt indicate the difference in the number of acute mortality between the two scenarios (NoPol and Euro 3) in country k at time t; acute mortality concentration-response relationship for pollutant j in country k at time t; the difference in concentrations between the two scenarios (NoPol and Euro 3) for pollutant j in country k at time t; the baseline mortality rate for country k at time t; and affected population group in country k at time t, respectively� We use the baseline mortality rate of 0�86% for Japan and the rate of 0�94% for China�
We also calculate mortality due to chronic exposure to PM2�5 for adults separately, following the methodology by Matus et al� (2011)� Based on the recommendation by Bickel and Friedrich (2005), we assume that chronic mortality only occurs in population groups of age 30 or older� We create five separate age-cohort groups (30-44, 45-59, 60-69, 70-79, and 80+), and calculate concentration-response function for chronic mortality in each group using the following equation:
CR kl
CM = CR k CM × M kl
M k CPL / M k
where CRCM and MCPL refer to concentration-response function for chronic mortality; and mortality rates for cardiopulmonary diseases, respectively� The subscript k is for each country and l for each cohort group� We use cardiopulmonary disease here, because the long-term exposure to PM2�5 is known to be associated with its increase in premature mortality (Pope et al�, 2002)�
Using the age-conditioned CR functions and the following equation, we calculate the number of mortality due to chronic exposure to PM2�5:
ΔCasesklCM = CR klCM × 1
2020− al × ΔCik i=al
∑⎛ ⎝ ⎜⎜
⎞
⎠ ⎟⎟ ×M kl
All × Pkl
where al and ΔCik indicate average birth year for a cohort group l, and PM2�5 concentration difference between the two scenarios in time i in country k� All the values used for this calculation are provided in Table 8�2� In order to compute ΔCik, we make an assumption that we can linearly interpolate the population-weighted concentration
values between 2000 and 2020 for both of the scenarios� We consider the uncertainty associated with this in Section 8�2�3�4�
8.2.3.3 Economic Valuation for Health Impacts Table 8�1 shows the valuation of each adverse health effects used in the model to calculate economic costs� In order to partition total costs to total service demand, labor lost and leisure lost, we assume the ratio of the money spent in each of these three fields for every health impact� The given valuation values are calculated based on the incurred costs of treating a specific illness in the country (e�g�, hospital visits) and also on survey data on WTP to avoid these adverse health impacts� Economic costs for service, labor, and leisure are calculated linearly by using the number of cases for each adverse health impacts, costs associated with them, and the ratio for service, labor and leisure, respectively� The cost estimates for China are taken from Matus (2005), and those for Japan are interpolated from the European values by using PPP� For mortality from acute exposure, we make an assumption that each exposure counts as a 0�5 years of life lost, and calculate a value by applying a statistical life year (VOLY) as recommended by Bickel and Friedrich (2005); 0�5 years is chosen as epidemiological study has shown that most vulnerable population are those that are affected by these acute exposures (Bickel and Friedrich, 2005)� We also calculate the lost labor and lost leisure time from mortality due to chronic exposure, based on the years that were lost from an expected 75-year life�
8.2.3.4 Uncertainty Analysis To account for uncertainties in health impacts and welfare calculation, we conduct uncertainty analysis for both� We use a probabilistic approach with Monte Carlo sampling method, as described in Selin et al� (2009) and Webstser et al� (2012)� For economic valuation of health, we assume that the values are always higher in Japan than in China� Using the uncertainty range shown in Table 8�1, we construct probability distributions of concentration-response and costs associated with adverse health impacts, with an assumption that concentration-response functions are correlated at r = 0�9� We
TABLE 8.2 Age, Average Birth Year, Mortality Rates for Cardiopulmonary Diseases, All Mortality Rates, and 2020 Population for Five Cohort Groups in China and Japan Used in EPPA-HE
calculate the uncertainty range using EPPA-HE, based on 400 sets of inputs for each scenario with varying concentration-response functions and with varying costs�
Figure 8�2 shows the spatial distribution of the annual difference between 2020 NoPol and Euro 3 surface O3 mixing ratios and PM2�5 concentrations� These indicate that the change in mixing ratio/concentration is most visible in the urban areas, where the vehicle number is the largest� These areas are also populated, and thus we see a large difference in numbers between area-weighted and population-weighted mixing ratios/concentrations, especially for PM2�5 as described in Table 8�3� For example, whereas the difference between 2020 NoPol and Euro 3 in area-weighted annual mean mixing ratio for O3 is 3�87 ppbv within China, that for population-weighted annual mean is 7�51 ppbv, almost double the value� The same is true for PM2�5 concentrations: the area-weighted value is 2�27 µg m-3, whereas the population-weighted value is 5�85 µg m-3� What is interesting, however, is the fact that we do not see the same impact within Japan� There is no difference between the two for O3, and there is even smaller difference for PM2�5, using the population-weighted average� This is because pollution is transported from China to Japan to places where population is not necessarily large for PM2�5, whereas O3 is transported equally to all of Japan� The difference in lifetime (approximately 2 weeks for the former and 1 month for the latter) results in these spatial distributions�
FIGURE 8.2 Population in 2000 and 2020 as well as the difference in O3 mixing ratio and PM2�5 concentration from the two scenarios in 2020�
Table 8�4 shows the reduced number of adverse health impacts by implementing the Euro 3 emission standards in China relative to no regulation� We present the values separately for each species for China and Japan, and find that there is a significantly larger number of reduced adverse health impacts in China, but we also find that the number in Japan is not negligible� Furthermore, we quantify the number of mortalities due to chronic exposure to PM2�5� Whereas EPPA-HE estimates over 42,700 people to be saved by the implementation of Euro 3 in China, it calculates 190 to be saved in Japan as well� Including both acute and chronic mortalities from surface O3 and PM2�5, EPPA-HE projects a reduction of 154,000 mortalities in China and 1,490 mortalities in Japan simply by China’s implementation of the Euro 3 vehicle emission standards�
Table 8�5 shows the welfare increase due to the reduction of surface O3 and PM2�5 in China and Japan, due to China’s implementation of the Euro 3 vehicle emission standards� Using EPPA-HE, we estimate China’s and Japan’s welfare increases of $33�4 and $3�9 billion, respectively, in 2020 due simply to China’s implementation of the Euro 3 vehicle emission standards� Similar to the health impacts, whereas 83% of
TABLE 8.3 Area-Weighted and Population-Weighted O3 Mixing Ratios and PM2.5 Concentrations in China and Japan for 2000, for the Two Scenarios in 2020, and the Difference between the Two 2020 Scenarios
TABLE 8.4 Reduced Adverse Health Impacts (2020 NoPol-2020 Euro 3) in China and Japan
the welfare gain in China originates in the reduction of PM2�5 concentrations, a little over 53% is due to the reduction of surface PM2�5 in Japan, and the rest due to the reduction of surface O3� We compare this welfare gain to 2010 GDP values in USD 2000, and find that it shares more than 1% of 2010 GDP for China� The value is small for Japan, and it is less than 0�1%, but we still find it to be important, as this is merely a positive spillover effect from China to Japan� We analyze the relative cost of these values as Japan’s environmental aid in the Discussions section�
Whereas mortalities due to PM2�5 exposure share 60% of all mortalities in China, they share only 31% in Japan� This is because O3 has a longer lifetime (~1 month) compared with PM2�5 (~2 weeks), and thus the impact of O3 mixing ratio is seen more vividly in Japan than that of PM2�5, as can be found in Figure 8�2� There are larger effects in Japan due to surface O3, because of reduced transport of O3 itself from China as well as the reduction in O3 precursor emissions in China leading to less O3 formation in Japan� Despite the larger adverse health impacts of PM2�5 in comparison with O3, there are larger health benefits due to reduced O3 mixing ratios within Japan� This analysis therefore confirms that the reduction of vehicle emissions has a larger impact than reducing its local pollution, as it also reduces regional pollution as a result of decreased CO, NOx, and VOC emissions as well as PM�
Comparing the economic impact, we find that Japan receives approximately 10% of China’s welfare gain as a result of the positive spillover from China’s vehicle emissions regulations� Due to the difference in their economic levels, the economic gain is only 0�07% of Japan’s GDP as opposed to China’s welfare gain by more than 1% of its GDP� Although Japan’s welfare gain appears small, we argue that this is substantial, especially considering that this is equivalent to the amount of Japanese ODA to China every year� Japan is the largest donor to China in the world, and since the start of its ODA program in 1979 until 2005, Japan has implemented approximately 3�1 trillion yen in loan aid, 145�7 billion yen in grant aid, and 144�6 billion yen in technical cooperation to China alone (MOFA, 2005)� Japan’s GDP in 2005 was 5,367,620 billion yen; 0�07% of the GDP value is 3�757 trillion yen, and this is
TABLE 8.5 Welfare Increase in Japan and China in 2020 due to China’s Implementation of the Euro 3 Vehicle Emission Standards (in billion USD 2000)
in the same order of magnitude as the total ODA that has been donated from Japan to China over the 26 years� In the recent years, Japan’s focus has been on providing environmental aid when giving ODA to China, and our analysis explains why Japan might be most interested in that�
Our estimates of the environmental costs for China lie within the range of the previous estimates� In 2007, the recent report by the World Bank and State Environmental Protection Administration (2007) estimated that China’s air pollution led to 1�3% of its GDP loss in 2003, in a conservative estimate� Others found a much higher rate, including Matus et al� (2011) that argued for 5% welfare losses from air pollution in China in 2005� We find that China potentially gains approximately 1% of its GDP simply by regulating vehicles, and it is not too surprising, considering the large share of vehicles as a source of CO, NOx, and VOC emissions�
Shindell et al� (2011) estimated the reductions in premature mortalities due to the implementation of Euro 6 standards in China compared with the current standards to be more than 100,000 deaths in China� We find that China avoids 42,700 chronic mortality and over 111,000 acute mortality by the implementation of Euro 3 standards� This is easily explained by the very little difference in the reduction of air pollutant concentration from Euro 3 to 6 standards� Saikawa et al� (2011) argue that holding everything else constant, the perfect implementation of the Euro 6 emission standards in China reduce CO, NMVOC, BC, and OC emissions only by an additional 1�6, 0�57, 3�0, and 0�68%, respectively, compared with that of the Euro 3 standards� This results in the similar number of premature mortality in the two studies�
We quantified the reduced adverse health impacts and the increased welfare in China and Japan due to China’s implementation of the Euro 3 vehicle emission standards in 2020� We used a fine-resolution WRF/Chem chemical transport model to analyze the change in surface air quality and the EPPA-HE model to calculate the impacts� We found that 154,000 mortalities in China and 1,490 mortalities in Japan are saved simply by China’s implementation of the Euro 3 vehicle emission standards, compared with no regulations in 2020� Furthermore, this policy implementation in China leads to the welfare increase of $33�4 and $3�9 billion in China and Japan, respectively� We quantified that China increases its welfare by more than 1% of its 2010 GDP by the implementation of the Euro 3 vehicle emission standards� Japan’s welfare gain is 0�07% of its 2010 GDP, but this is also significant, considering that it is merely a positive spillover effect from China�
We have assumed that emissions increase in other sectors except for the road transport sector in China, and we did not include emissions regulations in other areas� Vehicle emissions are unique in that we see reductions in both surface O3 and PM2�5 simultaneously� We found that China benefits more from the reduction of
PM2�5 due to its large adverse health impacts, but Japan benefitted equally from the two species, because of a longer lifetime of O3 and other O3 precursor species that were reduced in China� Our analysis reconfirms that China’s air pollution regulations potentially have large health benefits not only in China but also in Japan and other surrounding countries�
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