China Economic Review 31 (2014) 106–118
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China Economic Review
Welfare loss of China's air pollution: How to make personal vehicle transportation policy Su-Mei CHEN, Ling-Yun HE ⁎ College of Economics and Management, China Agricultural University, Beijing 100083, China
a r t i c l e
i n f o
Article history: Received 25 November 2013 Received in revised form 7 August 2014 Accepted 8 August 2014 Available online 28 August 2014 JEL classiﬁcation: C68 O13 Q51 Q53 Q55 Q58 Keywords: Welfare loss PM2.5 pollution China Personal vehicle transportation policy
a b s t r a c t Given China's notorious air pollution, particularly ﬁne particulate matter (PM2.5) pollution, a detailed understanding of socio-economic costs of air pollution and potential impacts of its abatement policies is crucial for policy-making if sustainable development is to be realized. To provide the ﬁrst study of its kind for China, this paper builds an integrated assessment framework based on a computable general equilibrium (CGE) model. We ﬁnd China's air pollution (PM2.5, ozone, and coarse particles ranging from 2.5 to 10 μm) to be a staggering threat to human health, economy and residential welfare. Furthermore, there is empirical evidence for much more importance of the PM2.5 issue. In addition, we investigate the impacts of alternative personal vehicle transportation policies. In terms of gross beneﬁts, the results indicate that the total substitution of plug-in hybrid electric vehicles (PHEVs) for the existing personal internal combustion engine vehicles (ICEVs) would be more beneﬁcial to national air quality and human health than the combination of stringent fuel economy and emission standards for ICEVs, even in the Chinese case of coalheavy electric grids. © 2014 Elsevier Inc. All rights reserved.
1. Introduction China's highly successful economic transition during the last three decades, with an ofﬁcially reported GDP growth rate of over 8% since 1980, has been accompanied by huge pressure on natural environment. The environment deterioration is already evident, particularly on the air quality issue in China nowadays. Seven of the world's ten most air polluted cities can be found in China in 2013.1 In terms of haze covering the whole country, ﬁne particulate matter with aerodynamic diameter smaller than 2.5 μm (PM2.5 for short) is a main cause; since the monitoring began in 2012, China's PM2.5 level has presented a range of 100–1000 μg/m3, which was at least 10 times higher than the reference standard (10 μg/m3) in the World Health Organization Air Quality Guideline (World Health Organization, 2006). Air pollution, particularly this PM2.5 concern,2 has been a trigger for extreme health damages. According to Global Burden of Disease Study 2010 (GBD 2010) by Lozano et al. (2013), PM2.5 pollution, the forth ranking risk factor in China, led to premature deaths of 1.2 million and more than 25 million years of life lost in 2010.3 Therefore, this paper focuses heavily on the PM2.5 issue. As the environmental damages accumulate and public awareness of environmental values rises with per capita income, ⁎ Corresponding author. E-mail addresses: [email protected]
(S.-M. Chen), [email protected]
(L.-Y. He). 1 The seven polluted cities in China were the following: Taiyuan, Beijing, Urumqi, Lanzhou, Chongqing, Jinan, and Shijiazhuang (source: http://www.echinacities. com/news/Seven-of-the-Worlds-10-Most-Polluted-Cities-are-in-China). 2 The PM2.5 pollutant is particularly dangerous because the matter is small enough to penetrate deep into the human lungs and enter the bloodstream of the human body. 3 Source: http://news.ifeng.com/shendu/nfzm/detail_2013_04/07/23929797_0.shtml/.
http://dx.doi.org/10.1016/j.chieco.2014.08.009 1043-951X/© 2014 Elsevier Inc. All rights reserved.
S.-M. Chen, L.-Y. He / China Economic Review 31 (2014) 106–118
China has continuously been criticized for promoting economic growth at the expense of natural environment. In this light, China has reached a crossroad, and it is a difﬁcult but vitally important task for China to balance economic growth and environmental protection. As the environmental Kuznets curve (EKC) depicts, environmental degradation ﬁrst increases with a nation's income during the early stages of economic growth, and then improves with income arriving at a certain turning point (Grossman & Krueger, 1995; Stern, Common, & Barbier, 1996). Intuitively, economic growth is believed to help abate environmental destruction,so developing countries make extensive use of the environment for the purpose of economic growth. In fact, it is the very idea that led to an extensive growth of the Chinese economy for the past three decades, coupled with increasing environmental degradation. Theoretically, the extensive economic growth is in resemblance with the viewpoint of ‘cowboy economy’ (Boulding, 1966): an open expanse full of environmental services (resources and opportunities) for the free taking. However, the consequences could be devastating. Song, Zheng, and Tong (2008) explain that the point of irreversible damage may be reached for a particular ecosystem before the turning point for environmental improvement. So the ‘cowboy economy’ of the past is obsolescent (Ayres, 1997); the ‘spaceship economy’ (Boulding, 1966), a foundation for the economic view of sustainability (Elliott, 2005), would be the target of economic transition. That is to say, as China tries to transform itself into a coordinating society (‘He Xie She Hui’ in Chinese), it is meaningful for the government to take measures to reduce environmental pollution. What are air pollution abatement policies to realize sustainable development? Actually, personal vehicle is one of the major anthropogenic emission sources. As Fig. 1 shows, the ﬂeet of the personal vehicles in China kept increasing rapidly along with the fast growing Chinese economy in 2000–2011. In this context, considerable policy concerns should concentrate heavily on personal vehicle transportation activity, especially in terms of vehicle exhaust contributions to poor air quality. Naturally, the following questions need to be carefully addressed. If no action is taken to mitigate air pollution, what socio-economic costs can result from anthropogenic air pollution? Then, how could such key variables as air quality, human health, GDP, and residential welfare move through alternative policy designs of personal vehicle transportation? To date, the effects of air pollution per se have been intensively discussed in China and other countries. For example, GBD 2010 (Lozano et al., 2013) is one of the signiﬁcant studies, which measures the number of diseases and premature deaths caused by PM2.5 pollution. There is also a body of literature on the air pollution abatement policy analysis. Chen, Ebenstein, Greenstone, and Li (2013) ﬁnd that China's Huai River policy with the laudable goal of providing indoor heat led to a 55% higher total suspend particulate (TSP) concentrations in Northern China and reductions in life expectancies of 5.52 years in this area by using a regression discontinuity design. Yet, these studies ignore the feedback effect from pollution to economic growth. Actually, pollution is theoretically not only a byproduct but also input of production (Shen, 2006). Furthermore, as we know, in real world, pollutant emission may reduce production through either the loss of workdays or excess medical expenses due to health damages caused by pollution. Thus, the economic growth and the environmental quality are jointly determined, and estimating the costs of environmental degradation without the feedback mechanism might probably produce biased estimates. In recent years, many studies on air pollution issues have increasingly focused on the health feedback based on a computable general equilibrium (CGE) approach (Matus, Yang, Paltsev, Reilly, & Nam, 2008; Matus et al., 2012; Nam, Selin, Reilly, & Paltsev, 2010; Selin et al., 2009; Wan, 2005). To date, one of the most representative and valuable frameworks is the MIT Emissions Prediction Policy Analysis—Health Effects (EPPA-HE) model, which has been applied to the future target or historical air quality level to estimate its associated economic impacts (Matus et al., 2008, 2012; Nam et al., 2010; Selin et al., 2009). However, those previous studies in China and many other countries have mainly focused on ozone (O3) and/or particulate matter of less than 10 μm (PM10 for short), but few on the PM2.5 pollutant. In particular, PM2.5 is known to be a much stronger risk factor for human health than PM10 (Cifuentes, Vega, Kpfer, & Lave, 2000; World Health Organization, 2013). Nevertheless, these existing literatures provide a useful guide to the analysis on the
Fig. 1. Personal vehicle ﬂeet in China, 2000–2011. Data source: China Statistical Yearbook 2012 (National Bureau of Statistics of P.R. China, 2012).
S.-M. Chen, L.-Y. He / China Economic Review 31 (2014) 106–118
impacts of air pollution as well as its abatement policies on economic growth and social well-being. Our study differs from these previous studies mainly by incorporating the source-speciﬁc inventory of anthropogenic emissions and the air concentration model into a framework, and then by applying it to China's PM2.5, O3 and coarse particles ranging from 2.5 to 10 μm (PM10–2.5)4 issues and their relevant personal vehicle transportation policy analysis. More speciﬁcally, the type of framework in this paper should allow the air pollution-related health damages to affect the reallocations of resources as well as demands of goods and service. To realize policy analysis, we need to establish an interface of estimating air quality from all types of energy use during production activities or daily life. We thereby add a source-speciﬁc inventory of anthropogenic emissions5 as well as an air concentration model. Grossman and Krueger (1995) conclude that pollution as a ‘joint-product’ of production activities is determined by three economic characteristics, i.e., scale, composition, and technical effect. Our study conducts policy design of personal vehicle transportation to improve air quality through the lens of technical effect. This is, both plug-in hybrid electric vehicles (PHEVs) and conventional internal combustion engine vehicles (ICEVs) are involved in policy scenarios (the details could be found in Section 3). Considering the objectives of this research, it is expected that this study will be useful in forming the personal vehicle transportation policy to mitigate air pollution as China searches for the optimal balance of economic growth and environment quality. This paper is organized as follows. Section 2 introduces the structure of an integrated assessment framework. Section 3 illustrates the design of counterfactual scenarios about personal vehicle transportation policies. Section 4 presents the results and discussions. Finally, Section 5 summarizes our results and offers some policy implications. 2. Structure of an integrated assessment framework For our analysis, we build an integrated assessment framework based on a CGE model to address China's air pollution issue. As mentioned earlier, we adopt three key air pollutants, i.e., O3, PM2.5, and PM10–2.5. Like the EPPA-HE model used in Matus et al. (2012), this framework accommodates pollution-generated health costs in a feedback, which in turn affect the economy from a general equilibrium perspective. We also apply a similar approach to estimate the economic burden of air pollution. Brieﬂy, a benchmark simulation is ﬁrst developed to replicate the observed economic performance which has already been distorted by health damages associated with the actual level of air pollution; we then estimate what economic performance would have been without any anthropogenic sources of pollutant emissions; ﬁnally, by comparing the economic results from these two scenarios, we can capture the socio-economic costs of air pollution. To realize macro-economic analysis of personal vehicle transportation policies as mentioned above, we incorporate the sourcespeciﬁc inventory of anthropogenic emissions and the air concentration model into the framework, where the ﬁrst main difference lies between our framework and the one used in Matus et al. (2012). In addition, the second main difference is that within our framework we focus heavily on China's PM2.5 pollutant emissions in addition to O3 and PM10–2.5. In this case, our modeling approach is to ﬁrst reproduce the initial equilibrium where health damages caused by the observed level of air pollution are included, and then to analyze what would have happened to economic performance with the health feedback under the counterfactual policy scenario. It is worth noting that this feedback is that of health related beneﬁts (or damages) from the counterfactual policy scenario, indeed the differences between health damages from the counterfactual policy scenario per se and our replication of actual health damages. Lastly, comparing economic performance from this counterfactual simulation with the benchmark, one gives us an integrated economic impact assessment of personal vehicle transportation policies. The integrated assessment framework that we use is brieﬂy described in Fig. 2. A static CGE model of the Chinese economy is built, capable of capturing key economic and resource allocation implications of air pollution-induced health damages (see Section 2.1). To calculate the high-resolution pollutant emissions stemming from energy consumptions, we develop the source-speciﬁc Chinese emission inventory, which is directly linked to the CGE model (see Section 2.2). Once air pollutant emissions are computed, we use a simpliﬁed air concentration model, i.e., the ﬁxed box model (De Nevers, 1995), to estimate the national pollution concentrations (see Section 2.3). For the sake of simplicity, exposed population is speciﬁed for the Chinese as a whole and not by the division of rural–urbanregions; for the inhaled dose, we do not distinguish indoor and outdoor air pollution. Given the national air quality and exposure population, we can calculate the number of cases of every health outcome, and then corresponding costs based on the existing epidemiological literature (see Section 2.4). Finally, the totals for all health outcomes (labor loss and medical expenditure) are passed into the CGE model as shocks in the total amount of labor supply available and the productivity of health service. 2.1. The CGE model In this paper, an 11-sector CGE model is employed as the main module. Disaggregated sectors include: agriculture, service, energyintensive industries, other industries, industrial transportation, household transportation, health services,6 crude oil & gas, reﬁned oil, 4 This paper focused on PM10–2.5 rather than PM10, for the fact that PM2.5 is smaller than 2.5 μm and PM10 smaller than 10 μm in terms of diameter. In this manner, it may avoid the double estimation. 5 Any anthropogenic emission factor is highly related with source characteristics such as fuel type, economic sector, energy technology and end-of-pipe controls, which are signiﬁcant but too often ignored in previous global and regional practices. For example, the PM2.5 emission factor from the Chinese commercial & institutional sectors would be 0.00251 g per kilogram of fuel oil, while that from industrial sectors is approximately close to 0.96 g per kilogram (Wang et al., 2005). Therefore, this paper employs a source-speciﬁc inventory of anthropogenic emissions. 6 Introduction of the health services sector allows us to capture the health effects related to air pollution. In our model, medical expenditure is represented by household consumption on the health services production.
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Fig. 2. Framework of integrated assessment.
coal, and electricity. Here, the household transportation sector that supplies the transportation needs of individual households for China is created with the approach of Karplus, Paltsev, and Reilly (2010). This model also consists of one representative household and the government with the factor endowments of labor and capital. The major characteristics of this model are summarized below. 2.1.1. Production We assume that there is a one-to-one relationship between the activity and commodity accounts. Fig. 3 illustrates the supply structure for all sectors. The rectangles contain the variables, while the rhombs indicate the functional forms used. A representative ﬁrm maximizes the proﬁt. She uses value added, energy aggregate and intermediate inputs to obtain aggregate intermediate input. Intermediate inputs enter in a Leontief structure with the capital–labor–energy (KLE) bundle at the top level. At the second level, the KLE bundle is constitutive of energy aggregate and value-added bundle following a constant elasticity of substitution (CES) function. At the lowest level, the single nest for energy aggregate that includes electricity, coal, crude oil & gas, and reﬁned oil would be represented by a Cobb–Douglas (C–D) function; such an aggregation technology is also employed in value-added, which is a bundle between labor and capital.
Fig. 3. Supply structure of CGE model.
S.-M. Chen, L.-Y. He / China Economic Review 31 (2014) 106–118
2.1.2. Household The representative household income is made up of labor income and capital returns from industries. After paying household income tax, obtaining various transfers from government and overseas, she gets the disposable income. A ﬁxed proportion (i.e., the marginal saving rate) of the household disposable income is spent on saving, and the rest on consumption of various commodities, which is simply described with a C–D preference. It is worth noting that the household consumes both purchased transport and own-supplied (i.e., private cars) in the transport nest: purchased transport comes from the industrial transportation (e.g., air travel, rail service) sector; own-supplied transportation service is provided from the household transportation sector, using the productions of other industries (purchases of vehicles) services (maintenance, insurance, etc.), and reﬁned oil sectors as intermediate inputs. 2.1.3. Government Government income mainly comes from various tax and transfers from other countries. Government expenditure is constitutive of government consumption, transfers to households and industries, and export tax rebate. 2.1.4. International trade The allocation of ﬁnal outputs from domestic production between exports and local markets is described by a constant elasticity of transformation (CET) technology. As for import, the commodity that is supplied domestically is a CES aggregation of domestic and imported commodities. 2.1.5. Model closure The government budget balance, foreign trade balance, and investment–savings balance are combined to close the model. For the government budget balance, government consumption is assumed to be exogenous; government saving is derived endogenously from the difference between government income and expenditure. For the foreign trade balance, the foreign savings and account currency are set as exogenous, with exchange rate endogenous. For the investment–savings balance, this is a savings-driven neoclassical model, where investment is endogenously determined by the income and the exogenous savings rate for the representative household. 2.1.6. Market clearing All market prices in the model are endogenous and adjusted to clear the market for all commodities, labor and capital factors. 2.1.7. Data source Subject to data availability, we set the base year as 2007. The core database is contained in a social accounting matrix (SAM), which is obtained mainly from China's input–output table of 2007 (National Bureau of Statistics of P.R. China, 2009), and China Health Statistical Yearbook 2008 (Ministry of Health of P.R. China, 2008). Besides the SAM, parameters are calibrated by equilibrium data based on the base year7 or referred to the current literature (Chen & He, 2013; He, Shen, & Xu, 2002; Wang, 2003). 2.2. Anthropogenic emission estimation As stated above, we estimate annual anthropogenic emissions with a source-speciﬁc emission inventory for China. In this methodology, emissions are categorized by fuel type (coal, fuel oil, and natural gas), and economic sector (residents, electric power, industry, commerce & institution, agriculture, and transportation). Thus, the resulting air pollutant emissions are estimated by the following equation: E¼
Am;n F m;n
where E refers to anthropogenic emissions, A the activity rate in the form of fuel consumption, and F the net emission factor; m and n refer to the fuel type and economic sector, respectively. Due to limited analysis of the O3 emission factor, this paper estimates anthropogenic emissions only for PM10–2.5 and PM2.5 pollutants in policy simulation. Here, F values for PM10–2.5 and PM2.5 are mainly from Wang et al. (2005) and Klimont et al. (2002) with our own adjustment; A values are attained from the simulation results of our CGE model. 2.3. Air quality estimation In order to estimate the national population exposure level of air pollutants, we need an air concentration model. As a matter of fact, this PM2.5 (or PM10–2.5) level in any location is highly related with both meteorological factors (e.g., temperature, humidity, wind velocity, and solar radiation) and emission factors (e.g., stack height, precise longitude and latitude). But taking the technique
7 The model is calibrated so that the initial equilibrium reproduces the base-year values from the SAM. Here,these values represent the observed economic performance in 2007, which was already affected by health damages from PM2.5 pollution.
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feasibility into account, these factors are difﬁcult to be applied into the situation of the whole China. To simplify the reality, we employ the ﬁxed box model whereby China is represented by a parallelepiped with the uniform pollutant dispersion (see Eq. (2)): c¼bþ
where c is the pollutant concentration in the entire site (μg/m3), b the background concentration (μg/m3), S the emission rate of the air pollutant (μg/s /m2), L the length (m), u the wind speed (m/s), and H the mixing height (m). In practice, the meteorological parameters (S, L, u, H) in Eq. (2) are difﬁcult to be speciﬁed in China at the national level. Data constraints force us to resort to simplifying assumptions. Thus we assume that these meteorological parameters are constant between the baseline situation (S1, C1) and the future situation (S2, C2). By eliminating these constant meteorological parameters, we use Eq. (3) to estimate the change of the national PM2.5 (or PM10–2.5) level: S2 C 2 −b ¼ S1 C 1 −b
where S1 and S2 are the baseline emission rate and the future emission rate (μg/s /m2), respectively; C1 and C2 are the annual average baseline level and the future level (μg/m3), respectively. Under the assumption of the uniform emission rate within a year, it is reasonable to think of the ratio of S1 and S2 as the ratio of the baseline annual emission (E1) and the future annual emission (E2), all of which can be deduced from Eq. (1). Due to data availability, one issue to deal with in estimating China's baseline PM2.5 (or PM10–2.5) level (C1) is the conversion between PM10 and PM2.5 (or PM10–2.5). According to Lvovsky (2000) and He et al. (2001), we use 0.65 as the PM10–PM2.5 conversion factor. In this sense, it is natural to attain the PM10– PM10–2.5 conversion factor of 0.35. Based on these conversion factors, the baseline PM2.5 (or PM10–2.5) concentration in 2007 (C1) is 44.70 μg/m3 (or 24.10 μg/m3), which is converted from the annual average PM10 concentration level in 2007 (Pandey et al., 2006); the PM2.5 (or PM10–2.5) background level (b) is estimated to be 39 μg/m3 (or 21 μg/m3) with the same manner as done for its baseline level. Many studies (Guo & Teng, 2004; Wan, 2005) use 60–90 μg/m3 as the PM10 background level in the north of China, and here we choose the smallest level (60 μg/m3) to be the national background level of PM10. Once those above data are attained, the future PM2.5 (or PM10–2.5) level (C2) in scenario simulations would be naturally computed. 2.4. Case computation and valuation Once the change of a pollutant's concentration level is determined, the consequent risks in health outcomes can be calculated using exposure–response (ER) functions from epidemiological studies. Unfortunately, such works have been sparsely done to date. Due to data availability, the quantiﬁable health effects in our analysis include mortality (acute and chronic), respiratory hospital admission, cardiovascular hospital admission, restricted activity day (for adults), work loss day (for adults), asthma attack, and bronchitis symptoms (only for children). All the ER functions in our study take a linear form, and do not assume any threshold effects. This linear form is widely applied in human health risk assessments (Guo, Cheng, Chen, Zhou, & Wang, 2010; Quah & Boon, 2003; Wang & Mauzerall, 2006). It is noteworthy that this method may overestimate the economic burden, if such thresholds, below which damages do not occur, exist at a pollution level beyond the background level. The mean values of the ER coefﬁcients used in this paper are listed in Table 1. Subject to the limit of the epidemiological literature, they are referred prior from China-speciﬁc studies (Hu et al., 2001; Ko et al., 2007; Xie et al., 2011) and also from international peer-reviewed literatures (Bell et al., 2008; Bickel & Friedrich, 2005; Pope et al., 2002). In particular, we compute the number of cases of air pollution-induced non-fatal health outcomes with Eq. (4): Morbidity
¼ ERij C j P
where ERij, Cj, and P refer to ER coefﬁcients for non-fatal health outcome i and pollutant j, the concentration level of pollutant j, and the entire population group, respectively. Similarly, we use Eq. (5) to compute the number of premature deaths from acute exposure: AM
Cj P M
refers to ER coefﬁcients for mortality from acute exposure to pollutant j, and M stands for overall mortality where ERAM j rate. In this study, we deal with premature deaths from chronic exposures to PM2.5 and PM10–2.5 pollutants as a function of age. Deaths from heart and lung diseases, which are the majority of premature deaths caused by chronic exposure to an excess particulate matter (PM) level (Holland, Berry, & Forster, 1999), are of much higher risk for older population groups in China (Fig. 4). Therefore, we follow
S.-M. Chen, L.-Y. He / China Economic Review 31 (2014) 106–118
Table 1 Exposure–response coefﬁcients. Impact categorya
Mean (95% C.I.)b
Mortality from acute exposure
PM2.5 PM10–2.5 O3 PM2.5 PM10–2.5 PM2.5 PM10–2.5 O3 PM2.5 PM10–2.5 PM2.5 O3 PM2.5 PM10–2.5 PM2.5 PM10–2.5 PM2.5 PM10–2.5
0.042% (0.003%,0.081%) 0.06% (0.04%,0.08%) 0.03% (0.01%,0.04%) 0.60% (0.40%,0.80%) 0.25% (0.02%,0.48%) 2.20E−04 (−1.20E−04,5.60E −04) 7.03E−06 (3.83E−06,1.03E−05) 3.54E−06 (6.12E−07,6.47E−06) 8.00E−04 (5.90E−04,1.01E−03) 4.34E−06 (2.17E−06,6.51E−06) 2.10E−03 (1.45E−03,2.74E−03) 4.29E−03 (3.30E−04,8.25E−03) 9.02E−02 (7.92E−02,1.01E−01) 5.41E−02 (4.75E−02,6.08E−02) 2.07E−02 (1.76E−02,2.38E−02) 1.24E−02 (1.06E−02, 1.42E−02) 6.60E−03 (1.18E−03,3.00E−03) 1.61E−03 (1.24E−04,3.10E−03)
Xie et al. (2011) Bickel and Friedrich (2005) Bickel and Friedrich (2005) Pope et al. (2002) Pope et al. (2002) Bell et al. (2008) Bickel and Friedrich (2005) Holland et al. (1999) Bell et al. (2008) Bickel and Friedrich (2005) Ko et al. (2007) Holland et al. (1999) Bickel and Friedrich (2005) Bickel and Friedrich (2005) Bickel and Friedrich (2005) Bickel and Friedrich (2005) Hu et al. (2001) Holland et al. (1999)
Mortality from chronic exposure Respiratory hospital admission
Cardiovascular hospital admission Asthma attack Restricted activity day (for adults)c Work loss day (for adults) Bronchitis symptoms (only for children)
a All coefﬁcients are for the entire population except as noted. The toxicity of PM10–2.5 is assumed to be equal to PM10 due to a limited number of epidemiological studies. b The ER coefﬁcients for chronic and acute mortality are expressed in mortality percent change per μg/m3 change of a pollutant's concentration, while the coefﬁcient for morbidity is measured in cases per year per person per μg/m3. c Restricted activity days include work loss days.
the approach of Nam et al. (2010) to calculate an age-conditioned ER coefﬁcient for chronic mortalities in China by using the following equation: CM
ERjn ¼ ER j
All M CPL n =M n
M CPL =M All
where ERCM and ERCM j jn are the unconditioned ER coefﬁcient for chronic mortalities and pollutant j (as displayed in Table 1) and ageCPL conditioned ER coefﬁcient for age cohort n and pollutant j, respectively; MALL (MCPL) and MALL n (Mn ) refer to mortality rate for all causes (or for cardiopulmonary diseases) for the entire population group and mortality rate for all causes (or for cardiopulmonary diseases) for age cohort n. As recommended by Bickel and Friedrich (2005), we assume that there exist chronic mortalities only in population groups of age 30 or older for the fact that illnesses from chronic exposure take many years to develop. The age-speciﬁc ER coefﬁcients
Fig. 4. Rate of cardiopulmonary mortality in China, 2007. Source: Data from Ministry of Health of P.R. China (2008).
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Table 2 Age-conditioned ER coefﬁcients for chronic mortalities, China. Pollutant
Age cohort 30–44
Computed from Pope et al. (2002) and Ministry of Health of P.R. China (2008). Adopted from Matus et al. (2012).
that we use are displayed in Table 2. Then, we estimate the numbers of chronic mortalities caused by PM2.5 and PM10–2.5 with the following equation: CM
ERjn C j Mn P n
where Mn and Pn refer to mortality rate and population size for age cohort n, respectively. Given that there are 260 workdays on average for a worker per year, we can get the workday loss resulted from deaths by multiplying the number of chronic mortalities aged 30 to 59 years8 by 260. Then the total loss of labor available would be estimated with the sum of workday loss from deaths and work loss days. In computing labor loss, we do not include the premature deaths from acute exposure, as some of them could be children or retirees. As a consequence, this approach may somewhat underestimate the economic impacts of air pollution. These numbers of cases are ﬁnally valuated in terms of RMB Yuan9 by using unit values displayed in Table 3. The approach of this analysis is prior based on the concept of willingness to pay (WTP), and also cost of illness (COI). For valuing the unit of work day loss, we follow Matus et al. (2012) in using the average wage level for the Chinese workers, which are endogenously determined in the CGE model. Although uncertainties exist in the monetary valuation, we believe that this calculation can provide useful information on the current health costs and the future beneﬁt of different pollution mitigation pathways. 3. Policy scenarios 3.1. Policy 1 scenario: total substitution of PHEVs for the existing personal ICEVs As mentioned above, in the face of such urgent air pollution challenges on human health and economic structure adjustment, new energy vehicles (NEVs) have been suggested as an environmental friendly alternative to ICEVs that could enter the personal vehicle market. Given that Battery Electric Vehicles (BEVs) and Fuel Cell Vehicles (FCVs) still have large technological hurdles to overcome to come near to commercial viability (Sandoval, Karplus, Paltsev, & Reilly, 2009), here we choose the PHEVs, which typically rely on battery power over a ﬁxed distance and can be recharged from electric grids, as our major policy concern. It is generally accepted that the reduction of PHEVs' oil consumption is straightforward. Nonetheless, the environmental impact of PHEVs in China is complicated because Chinese electricity is still generated primarily from coal. Hence, this paper allows air pollution implication of PHEVs to be analyzed by simulating a counterfactual scenario: PHEVs fully replace the existing ﬂeet of personal ICEVs (i.e., the total substitution of PHEVs for personal ICEVs). In other words, it is the personal vehicle transportation supplied by a PHEV. To simulate this Policy 1 scenario, we impose a simple shock by replacing all existing inputs to ICEVs with those to PHEVs. In this scenario, the ownership costs of ICEVs are those that act as intermediate inputs to the household transportation sector. For the PHEV, 60% of miles driven are assumed to be supplied by electricity, while the remaining 40% are supplied by reﬁned oil. In general, intermediate inputs to the PHEV sector for households include electricity and reﬁned oil (fuel) as energy inputs, services, and the vehicle itself. Following the approach of Karplus et al. (2010), we start from the fuel consumption of personal transportation (ICEVs) in the SAM and the ﬂeet average fuel economy assumption of 9.02 L per 100 km (Huo, He, Wang, & Yao, 2012), and then calculate the implied vehicle-miles traveled; if the ﬂeet has instead been PHEVs and driven the same miles, we value electricity and fuel requirements. The PHEV is assumed to require 0.48 kW h per kilometer consistent with the previous studies (Duvall, Knipping, & Alexander, 2007), or 4.63 L per 100 km which is half of the ICEV fuel economy. Given these assumptions on technical efﬁciency, the required electricity and fuel for the hypothetical 2007 ﬂeet of PHEVs are determined. The purchase cost of the PHEV is assumed to be higher by 30% than that of the ICEV.10 Service costs for the PHEV are assumed to be the same as those for the ICEV. Therefore, the ownership costs of the ICEV and PHEV for China are shown in Table 4. 8 Here, we assume that a worker would generally retire at his or her age of 59 in China. Since chronic mortalities are assumed to only occur at the age of 30 or older in this paper, it is plausible and attainable to compute the number of worker deaths aged 30–59 years. 9 Unless mentioned otherwise, RMB Yuan refers to 2007 RMB Yuan throughout this paper. 10 Some studies (Graham, 2001; Simpson, 2006) estimate that the PHEV is likely to be more expensive than a conventional vehicle by 22%–114%. The assumption of 30% higher purchase cost for the PHEV in this study is consistent with current estimates of the technology potential. In addition, we also simulated the complete replacement of the PHEVs for ICEVs under other different purchase costs of the PHEV. This is, the purchase cost of the PHEV is higher than the ICEV by 15% and 80%, respectively. The results on improving air quality and human health are almost close to these reported ones here.
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Table 3 Unit value for various health endpoints in China (RMB Yuan, 2007). Outcome a
Respiratory hospital admission Cardiovascular hospital admissiona Restricted activity day (for adults)b Asthma attackb Bronchitis symptoms(only for children)b
Per admission Per admission Per day Per case Per case
5767 8771 76 34 30,738
COI COI WTP WTP WTP
Ministry of Health of P.R. China (2008) Ministry of Health of P.R. China (2008) Kan and Chen (2004) Hammitt and Zhou (2006) Kan and Chen (2004)
a In general, unit value for hospital admission includes hospital admission costs, fees for service, and lost wages during days spent in hospital. As the work loss day has been considered as a single health endpoint, here unit value for hospital admission (respiratory and cardiovascular) is equal to the sum of hospital admission costs and fees for service. b Unit values are converted from the listed WTP surveys based on China's per capital income difference between the national level in 2007 and the level of the selected city in the surveyed year; the income elasticity is assumed to be 1.
Table 4 ICEV and PHEV ownership costs in China (billions of RMB Yuan, 2007).
We assume that electricity price remains constant at RMB0.693 Yuan per kW h, according to State Electricity Regulatory Commission of P.R. China (2008). We assume that reﬁned oil price remains constant at RMB5.3 Yuan per liter, according to the 2007 oil price (date source: http://www.cs.com.cn/xwzx/01/d30/06/ 200806/t20080610). Thus, for the Policy 1 scenario, the fuel use from personal vehicle transportation would drop largely by RMB62.8 billion Yuan. c The results are the sums of the costs of electricity, fuel, vehicle and services. The total costs indicate that the total ownership cost of PHEV is much higher than that of ICE-only vehicle by RMB22.2 billion Yuan for the Policy 1 scenario. b
3.2. Policy 2 scenario: Tighter FES and emission standards for the existing personal ICEVs In China, ICEVs have remained the dominant personal vehicle technology for decades and had the support of an extensive infrastructure. In contrast, it needs a long time for NEVs to enter the personal vehicle market mainly due to their technological immaturity and poor infrastructure. In this sense, a suite of policies concerning personal ICEVs designed to improve air quality, such as fuel economy standards (FES) and emission standards, are required for China in the near term. In terms of the ﬁnal target on ﬂeet fuel economy, China has proposed an aggressive standard for 2015 equivalent to 0.07 L per kilometer, a signiﬁcant decrease of 20% from the current level. National Emission standards V, the other mainstay of transport policy, suggests a stringent 82% decrease of PM emissions from light-duty vehicles.11 So this paper attempts to explore the potential impacts under a tighter standards scenario for personal ICEVs: the combination of a 20% improvement on fuel economy, a 50% reduction of PM2.5 emission and a 30% reduction of PM10–2.5 emission in terms of grams per liter.12 4. Simulation results 4.1. Socio-economic costs of anthropogenic air pollution and decomposition analysis To estimate the socio-economic costs of China's anthropogenic air pollution in 2007, we need to simulate two scenarios as mentioned in Section 2. One scenario is the initial equilibrium as a benchmark, where the PM2.5, PM10–2.5 and O3 levels are set at the historical ones in 2007, i.e., 44.70 μg/m3, 24.10 μg/m3 (as stated in Section 2.3) and 106.06 μg/m3, respectively. Here, this historical level of O3 for 2007, for which China's monitored O3 level does not exist, is assumed to be comparable to that for 2006, when the GEOSChem model of tropospheric chemistry simulated the ozone concentration (Wang, Zhang, Hao, & Luo, 2011). The other one is a counterfactual case, in which there is no any air pollution stemming from human activity. In other words, the counterfactual scenario sets the O3 level at 94.49 μg/m3 according to Wang et al. (2011), the PM2.5 level at 39.00 μg/m3 and the PM10–2.5 level at 21.00 μg/m3 (mentioned in Section 2.3). The socio-economic costs of air pollution are shown in part I of Table 5. It is important to emphasize that anthropogenic air pollution has posed a heavy threat on human health; the portions of pollutant concentrations exceeding the natural levels caused over 0.371 million residents of China to lose their lives in 2007. From a general equilibrium perspective, if health damages (i.e., premature deaths and illness, the details can be seen in the next paragraph) were passed into the macro-economy, the labor supply available would be largely reduced by 133.115 million workdays. In general terms, labor is known to be one of the basic resources; its decrease naturally weakens the economy growth without consideration of production efﬁciency. Particularly in the case of China's transitional economy, the Lewis turning point characterized as economic development with labor shortage is coming in the new 11
It is the ﬁrst time to target PM emission in the wake of poor air quality, and not yet ﬁnalized now. Given that National Emission standards V does not specify the targeted mitigation of PM2.5 or PM10–25 emission, this paper simply assumes a 50% reduction of PM2.5 emission and a 30% reduction of PM10–2.5 emission from vehicles in terms of grams per liter of gasoline. 12
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Table 5 Socio-economic costs and health damages induced by anthropogenic air pollution, China, 2007.a I
Acute exposuref Chronic exposuref Respiratory hospital admissiong Cardiovascular hospital admissiong Restricted activity day (for adults)f Work loss day (for adults)f Asthma attackg Bronchitis symptoms (only for children)g Sub-total of health costs by pollutant Total a b c d e f g h
21 245 1656 6025 407,597 93,539 15,815 12,923
1.132 2.598 18.163 61.454 39.585 8.608 9.146 405.860 537.938h(87%) 618.052 (100%)
17 55 28 17 133,149 30,518 – 1717
0.881 0.591 2.976 2.965 12.929 2.809 – 55.589 75.931h(12%)
31 – 54 – – – 65,582 –
1.642 – 0.312 – – – 2.229 – 4.183h(1%)
All data in this table are attained by the scenario simulations. Unit: million cases. Unit: million workdays. Unit: billions of RMB Yuan. Unit: thousands of cases. Wage loss category. Medical expenses category. The cost of work loss days is not considered in computation of sub-total and total costs because restricted activity days include work loss days.
century (Zhang, Yang, & Wang, 2011). The loss of labor supply caused by air pollution in China somewhat may seem to add insult to injury. China's real GDP dropped by 1.344%, or in absolute value of RMB361.468 billion Yuan. In addition, the reduced labor supply would pull the labor income down. This, along with the increased expenditure on health services, would ﬁnally lower the disposable income by RMB585.428 billion Yuan (part I in Table 5). In this case, households in China would suffer from welfare losses. Real purchasing power measured by Hicksian equivalent variations (EV) dropped as huge as about RMB227.649 billion Yuan in 2007. It is noted that we do not consider the consumption of health services in valuation of welfare, because the increased consumption of health services resulted from bad health outcomes makes citizens worse off. Further insights into the pollution-induced health damages can be gained by decomposing these by pollutant, health outcome, and cost category. Part II of Table 5 presents the increased number of cases of pollution-induced fatal and non-fatal outcomes and their monetary values in 2007. For the health outcomes, the cases of chronic mortalities from anthropogenic air pollution were over 4 times larger than those from acute exposure, conﬁrming the great severity of chronic exposure. In particular, of the analyzed PM2.5-induced health outcomes, asthma attack had the highest incidence rate, and then followed by bronchitis symptoms; cardiovascular hospital admission accounted for the most fraction of the non-fatal health costs. For the pollution health costs, it is computed by the sum of medical expenses and wage loss caused by illness or premature deaths. We estimate that pollution health costs in 2007 induced by PM2.5, PM10–2.5 and O3 concentrations exceeding their background levels were RMB618.052 billion Yuan. Around 87% of the total costs were from excess PM2.5 concentrations, and the remaining parts can be attributed to PM10–2.5 (12%) and O3 (1%), respectively. These estimates provide empirical support for the much more importance of the PM2.5 issue than PM10–2.5 and O3 ones. At the same time, for all these three pollutants, we note that the medical expenses category accounted for the largest portion of health costs, followed by wage loss albeit at different proportions, and the morbidity category was estimated to generate a larger amount of pollution health costs than the mortality category. In all, these numerical estimations as crucial inputs to policy-making indicate that air pollution, particularly PM2.5 pollution, has caused enough serious health damages to hinder the sustainable development of China if no action has taken to mitigate its emissions. However, it is not enough to satisfy the needs of policy-makers. It is necessary to further investigate how to facilitate sustainable development through policy design of personal vehicle transportation. 4.2. Scenario analysis on Policies 1 & 2 The key impacts of Policy 1 and Policy 2 scenarios on human health and socio-economy are shown in Table 6.13 We can easily ﬁnd that personal PHEVs have considerable potential to deal with China's poor air quality as indicated by the decreased levels of PM2.5 (0.102 μg/m3) and PM10–2.5 (0.025 μg/m3) for Policy 1. However, in the case of Policy 2 scenario, the reduced levels of PM2.5 and PM10–2.5 pollutants are relatively small, i.e., 0.056 μg/m3 and 0.009 μg/m3, respectively. Note that, many studies (Granovskii, Dincer, & Rosen, 2006; Yan & Crookes, 2010) believe that environmental emissions of electric vehicles largely depend on the source of the electricity from a life cycle perspective. Interestingly, our result implies that when personal PHEVs are recharged from China's current 13 As explained in Section 2.2, the analysis of the anthropogenic O3 emission factor is limited. So this paper estimates anthropogenic emissions only for PM2.5 and PM10–2.5 pollutants in policy simulation. In other words, we do not consider the impacts of policy scenarios on the O3 level and its associated human health. This method may underestimate the policy impacts, but provide useful information for policymakers.
S.-M. Chen, L.-Y. He / China Economic Review 31 (2014) 106–118 Table 6 Key health and socio-economic impacts of Policy 1 & 2 scenarios in China.a
PM2.5 concentration (μg/m ) PM10–2.5 concentration (μg/m3) Population death (thousands of cases) Labor available (thousands of days) Medical expenditure (billions of RMB Yuan) Real GDP (billions of RMB Yuan) Welfare gains (billions of RMB Yuan) a
−0.102 −0.025 −5.331 2001.740 −8.597 −10.368 8.430
−0.056 −0.009 −2.882 1201.044 −4.760 6.340 11.483
Changes are shown in the absolute terms from the baseline level. All data in this table are attained by the scenario simulations.
coal-heavy electrical grids, the Policy 1 scenario would also bring about a sizable improvement on national air quality. In this sense, the simulation result has shown an important air pollution implication of PHEVs in China. Even small reductions of population exposure to large risks will yield substantial health gains (Rose, 2001). As displayed in Table 6, Policy 1 contributes to more stringent pollution mitigation than Policy 2, and the beneﬁt from Policy 1 is much larger in terms of health damages avoided. Taking lives saved for example, Policy 1 beneﬁts 5.331 thousand cases, almost 84.975% more than Policy 2 does. Owing to the signiﬁcant improvement of human health, the demand for medical service, i.e., medical expenditure, would be reduced by much more for Policy 1. Theoretically, the additional supply of labor resource would contribute to real GDP gains. However, as shown in Table 6, the real GDP effect for the Policy 1 scenario is not positive (−RMB10.368 billion Yuan). The main reason is that Policy 1 brings about a signiﬁcant decrease in the output of health services (−RMB8.597 billion Yuan, Table 6) and a large reduction of fuel use from personal vehicle transportation (−RMB62.800 billion Yuan, Table 4), the total of which are too substantial to be offset by the output beneﬁts of other sectors from the increasing labor resource. In this case, it makes no sense to care much about this real GDP loss in terms of improved human health and energy conservation. What's more, we also measure the welfare beneﬁts in terms of EV, which capture consumption changes of all commodities (except health services as stated above) due to net wage gains. It is estimated to increase by RMB8.430 billion Yuan (Policy 1), or RMB11.483 billion Yuan (Policy 2). The relatively less welfare gains of Policy 1 are mainly due to the fact that the much higher ownership cost of the PHEV than ICE-only vehicle (by RMB22.200 billion Yuan, Table 4) crowds out the consumptions of other necessary commodities, which generally account for a considerable portion of the total household consumptions. Therefore, from the perspective of welfare gains, our results suggest the need to offer a PHEV subsidy by the Chinese government. 5. Conclusions and policy implications This paper assesses the socio-economic costs of China's air pollution (PM2.5, O3 and PM10–2.5), and then the potential impacts of personal vehicle transportation policies, using an integrated assessment framework based on a CGE model. Results and some important policy implications are summarized as follows.14 (1) It is necessary for the Chinese policy makers to urgently address anthropogenic air pollution, particularly PM2.5 pollution, which has staggeringly damaged human health, the economy and residential welfare. In the case of 2007, due to the excess emissions of PM2.5, O3 and PM10–2.5 pollutants, China experienced a staggering GDP loss of about RMB361.468 billion Yuan and a welfare loss of about RMB227.649 billion Yuan. In a transitional economy, this timely valuation is to help both the residents and the government to voice more concerns about the sustainability of China's rapid economic growth than before. (2) Financial supports to personal PHEVs are important in the current state of China. Scenario simulations show that the total substitution of PHEVs for China's existing personal ICEVs (Policy 1) will improve air quality, human health and thus well-being, even in the current case of coal-heavy electric grids. This provides evidence for the positive environmental potential of PHEVs in China. However, such an environmental improvement might come with a sizable real GDP loss, which does not imply that there is no need of personal PHEVs. As Beirne, Beulen, Liu, and Mirzaei (2013) point out, to minimize the negative externalities created by China's excessive growth, one policy focus should be on how to slow down the pace of economic growth to a sustainable level. In addition, welfare gains from Policy 1 are relatively small compared with Policy 2, mainly due to high ownership cost of personal PHEVs. Accordingly, the country is still expected to offer ﬁnancial supports to commercialize personal PHEVs. (3) It is also essential for policy interventions to tighten FES and emission standards for personal ICEVs. Our ﬁndings show that air quality, health outcomes and residential welfare tend to beneﬁt from tighter FES and emission standards for personal ICEVs (Policy 2). It is not to deny but to conﬁrm the necessity of stricter FES and emission standards. Also, comparison with the Policy 1 scenario reveals the relative superiority of PHEV adoption in terms of environmental and health beneﬁts. Lastly, we ﬁnish on a note of caution. We see this paper as a ﬁrst attempt to estimate the socio-economic costs of air pollution, including PM2.5 pollution, and potential impacts of its abatement policies in China. In particular, a complete policy analysis should 14
Our sensitivity analysis shows that the valuations of socio-economic costs are highly sensitive to ER coefﬁcients, but our general conclusions still hold.
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