Fine particulate pollution (PM2.5) is a leading mortality risk factor in the People's Republic of China (PRC) and many Asian countries. Current studies of PM2.5 mortality have been conducted at the national and provincial levels, or at the grid-based micro level, and report only the exposure index or attributable premature deaths. Little is known about the welfare implications of PM2.5 mortality for urban areas. In this study, we estimate the total cost of PM2.5 mortality, the benefit of its reduction achieved through meeting various air quality targets, and the benefit of mortality reduction achieved through a uniform 10 micrograms per cubic meter decrease in PM2.5 concentration in the urban areas of 300 major cities in the PRC. Significant heterogeneity exists in welfare indicators across rich versus poor and clean versus dirty cities. The results indicate that cities in the PRC should accelerate the fine particulate pollution control process and implement more stringent air quality targets to achieve much greater mortality reduction benefits.

Air pollution, especially the ambient fine particulate matter known as PM2.5, which are particulates with aerodynamic diameter ≤2.5 micrometers (μm), is a risk to human health (Dockery et al. 1993, Pope et al. 2002). The benefit of reduction in premature mortality risk attributable to lowering PM2.5 comprises the vast majority of the overall benefit of air pollution control policies, and has been used to inform policy efficiency in various regulatory contexts (United States Environmental Protection Agency 2006, 2011). For example, the cost–benefit analysis of the Clean Air Act in the United States (US) suggests that in 2020 the total annual benefit will reach $2 trillion (in 2006 prices), more than 30 times the total compliance costs; of the total benefit, 85% is due to reductions in mortality attributable to ambient PM2.5 (hereafter PM2.5 mortality) (United States Environmental Protection Agency 2011).

Rapid economic growth powered by surges in fossil fuel consumption and urbanization in many developing countries in Asia, such as the People's Republic of China (PRC) and India, have led to increases in air pollution and adverse health outcomes that are much more severe than in developed countries. Of the 1.37 billion people living in the PRC, 83% live in areas where the PM2.5 concentration exceeds the PRC's ambient air quality standard (AQS) of 35 micrograms per cubic meter (μg/m3) (Liu et al. 2016a). The northern, eastern, and central regions of the PRC have been exposed to annual PM2.5 concentrations ranging from 40 μg/m3 to 100 μg/m3. Meanwhile, fine particulate pollution was associated with 4 million premature deaths worldwide in 2015, including 1 million in the PRC and 1 million in India (Global Burden of Disease Study 2015).

Recently, fine particulate pollution and its impacts have become national concerns and are now among the top political priorities in the PRC (Young et al. 2015; Jin, Andersson, and Zhang 2016). Stringent policies have been implemented on a national scale. Yet, the economic benefit of PM2.5 control is unclear, and the effectiveness and efficiency of current policies is still being debated (detailed information is provided in section II). The targets of PM2.5 concentration reduction, the prioritization of local interventions across sites and sectors, and the pace of the fine particulate pollution control process have been frequently questioned (Liu 2015; Jin, Andersson, and Zhang 2016, 2017). A sound economic analysis of the benefit of fine particulate pollution control is urgently needed to inform efficient policy design and implementation in the PRC.

In this study, we measure the benefit of fine particulate pollution control by quantifying PM2.5 mortality and its welfare implications for the census-registered population in the urban areas of 300 cities at the prefecture level and above (hereafter major cities) in the PRC.1 We develop an analytical framework that only requires publicly available data on PM2.5 concentration and official statistics. We focus on analyzing three monetized benefits of PM2.5 mortality reduction: (i) the theoretical maximum benefit, achieved by reducing PM2.5 concentration from the current level to zero, which is identical to the total cost of PM2.5 mortality;2 (ii) the total benefit of mortality reduction achieved by meeting the World Health Organization's (WHO) Air Quality Guidelines (AQG) (TBAQG) and three interim targets for fine particulate pollution (TBIT-1, TBIT-2, and TBIT-3);3 and (iii) the benefit of mortality reduction achieved by a uniform 10 μg/m3 decrease in PM2.5 concentration (UB10).

Three main considerations motivate our research design. First, we focus on clearly defined city-level jurisdictions across the PRC rather than on provinces or grid-based maps with square cells. Most provinces in the PRC are vast territories, and the distributions of income, population, and pollution are highly uneven within each province. Therefore, the estimates aggregated at the national or provincial levels used in prior studies (see, for example, World Bank 2007, 2016; Xie et al. 2016a) may mask heterogeneity in PM2.5 mortality. Furthermore, because it is the municipal governments that implement policies at the local level, it is more useful to provide estimates at the level of these micro jurisdictions. One may naturally think that the recent grid-based studies mapping PM2.5 mortality distribution (see, for example, Lelieveld et al. 2015, Liu et al. 2016a, Xie et al. 2016b) are suitable for this purpose, but it is often burdensome to match the estimates of multiple cells with the borders of cities and districts.4 Therefore, in this study we directly estimate outcomes at the level of city jurisdictions.

Second, we focus on the urban areas of major cities.5 The greatest benefit of a reduction in PM2.5 mortality will occur in urban areas because they have larger populations, more fine particulate pollution, and higher income levels, which are associated with a greater willingness to pay to reduce mortality risks. Further, in developing countries like the PRC, databases of monitored PM2.5 concentrations and official statistics on socioeconomic status, which are essential for the validity of welfare estimates, are much more comprehensive for urban areas than rural areas. For simplicity, in the rest of this paper, we use “cities” to refer to the urban areas of major cities, unless indicated otherwise.

Third, in addition to considering the total cost and total benefit, we analyze the benefit of mortality reduction achieved by a uniform 10 μg/m3 decrease in PM2.5 concentration in cities across the PRC. The distribution of UB10 across cities is of both polity and research relevance. Most cities in the PRC are exposed to fine particulate pollution levels far above WHO's first interim target (35 μg/m3), and it may take decades of effort to meet this standard. Therefore, the economic benefit of a unit of pollution reduction, for example, a 10 μg/m3 PM2.5 concentration reduction, is policy relevant. Efficient fine particulate pollution control requires that the cost of reducing PM2.5 pollution by one unit should be lower than the estimated UB10 for that unit change. However, the estimation of UB10 is complicated by two issues. Recent evidence shows that the concentration response (CR) function between fine particulate pollution and mortality risks may be nonlinear (concave), especially at high PM2.5 concentrations (Burnett et al. 2014, Pope et al. 2011). The concave CR function indicates that in cites with the same population density, the same reduction in PM2.5 concentration will lead to less mortality risk reduction in cities with higher concentrations than in cleaner cities, which is counterintuitive and has raised environmental equity concerns (Pope et al. 2015). However, citizens in dirtier cities can sometimes have higher incomes and may therefore be more willing to pay to reduce health risks. These contradictory effects make the real distribution of UB10 among different types of cities a complicated empirical question.

This study contributes to the growing literature on the economic analysis of the health impacts of air pollution in developing countries. In particular, our estimation framework uses only publicly available data; is based on clearly defined jurisdictional urban areas; and considers the total cost of PM2.5 mortality, the benefit of meeting different air quality standards, and the benefit outcomes of a uniform reduction in PM2.5 concentrations. Our study focuses on the outcomes of annual PM2.5 exposure in 2016, but the same approach can be applied to other years and countries. This framework enables us to not only evaluate the environmental benefits of pollution control, but also to provide stakeholders with an easy-to-use tool for generating inputs for policy impact analyses.

Our analysis suggests that ambient PM2.5 in the urban areas of major cities in the PRC caused 0.67 million premature deaths in 2016. The percentage of deaths attributable to fine particulate pollution in urban areas is twice the national average, indicating that urban residents face considerably higher mortality risks than the general population. For 2016, the aggregated total cost of PM2.5 mortality in major PRC cities was about CNY1,172 billion.6 The average per capita cost of PM2.5 mortality is CNY2,255. The aggregated benefit of mortality reduction from a uniform 10 μg/m3 decrease in PM2.5 concentrations in these cities was about CNY141 billion in 2016, which is equal to a per capita UB10 of CNY321. Our results show significant heterogeneity across cities with different characteristics. Cities with medium to high PM2.5 concentrations, high per capita income, and large populations have the highest total cost. Cities with lower PM2.5 concentrations, high per capita income, and large populations can realize the most benefit from a uniform 10 μg/m3 decrease in PM2.5 concentrations. Meeting the PRC's current AQS of 35 μg/m3 will realize a very limited reduction in PM2.5 mortality; therefore, all cities in the PRC should aim at reducing fine particulate pollution to levels below the stringent WHO third interim target and AQG.

The rest of the paper is organized as follows. Section II describes the available data and the challenges of fine particulate pollution in the PRC. Section III develops an analytical framework to quantify the premature deaths from fine particulate pollution and the welfare measures of total cost, total benefit, and the benefit of a uniform 10 μg/m3 decrease in PM2.5 concentration. Section IV presents the results and section V discusses the implications for the literature and policy makers. Section VI concludes the paper. The list of technical terms used in this paper is found in the Appendix.

We collect three sets of publicly available data to form a city-level, cross-sectional data set for all the major cities in the PRC in 2016. They include the following data: (i) city-specific annual average PM2.5 concentration, (ii) urban census registered population sizes, and (iii) urban per capita disposable income.7 The fine particulate pollution data are available from the Qingyue Open Environmental Data Center.8 The urban population and income data are collected from provincial statistical yearbooks.9 With this data set, we can assess PM2.5 mortality, the various welfare indicators, and their heterogeneity across cities with different characteristics. Before presenting our formal estimation, we provide an overview of the PRC's ambient fine particulate pollution, recent policies, and how current pollution levels compare to various air quality targets.

The PRC's economy has been growing rapidly for decades; uneven regional development has concentrated most of the population, fossil fuel consumption, and vehicles in city clusters in the eastern and central PRC. As a result, citizens in these areas enjoy much of the benefit of economic development but have more severe environmental problems such as air pollution. Cities in the northern PRC are exposed to more severe air pollution than the national average, mainly because of the coal consumed by heavy industries and by urban central heating during the winter in these areas. Furthermore, rural households in the northern PRC use coal and biomass for winter space heating. Due to incomplete combustion, these fuels make substantial contributions to ambient fine particulate pollution throughout the region (Liu et al. 2016b). In addition to coal, vehicle emissions are important contributors to pollution in all cities in the PRC (Shao et al. 2006, Tsinghua University 2006). In megacities such as Beijing and Shanghai, vehicles make the greatest contribution to local fine particulate pollution, exceeding other sources such as coal combustion, road and construction site dust, and industry processes (Beijing MEPB 2014, Shanghai MEPB 2015). Finally, regional-scale transported PM2.5 also affects cities in the plains (Xu, Wang, and Zhang 2013), whereas for the basin cities in the central PRC such as Chengdu and Chongqing, meteorological conditions that are unfavorable to pollutant diffusion worsen local pollution (Liang et al. 2016).

In January–February 2013, a winter-long haze caused by extremely high PM2.5 concentrations enveloped the whole northern and eastern PRC. Fine particulate pollution and its impacts on health became, for the first time, a nationwide concern (Chris and Elizabeth 2016). The strong political will to control PM2.5 pollution was seen in the quick and stringent response from the Government of the PRC. The China State Council (2013) issued the Air Pollution Prevention and Control Action Plan, 2013–2017 (hereafter the Action Plan), which contains compulsory regional air quality improvement goals and 10 key tasks. The goals were largely uniform within each region but varied between regions: the Action Plan required the Beijing–Tianjin–Hebei, Yangtze River Delta, and Pearl River Delta regions to reduce their annual average PM2.5 concentration by 25%, 20%, and 15%, respectively, between 2013 and 2017, whereas the rest of the PRC was assigned the more relaxed goal of reducing particulates with aerodynamic diameter ≤10 micrometers (PM10) by 10% over the same period. Faced with these mandatory air quality improvement goals, local authorities implemented a variety of regulations. Although improvements were observed at the end of the 5-year policy window, it was difficult for the northern region of Beijing–Tianjin–Hebei, which has much higher PM2.5 levels (Tsinghua University and Clean Air Alliance of China 2014), to reach the policy goals even though regulations with very high compliance costs had been implemented (Jin, Andersson, and Zhang 2017; Wang 2017). However, the cleaner region of the Pearl River Delta reached its goals easily (China Environmental Protection Association 2016).

In the long term, the PRC's cities must meet the challenge of more stringent air quality targets. WHO AQG for annual average PM2.5 concentration is 10 μg/m3.10 WHO also defines three interim targets (IT-3, IT-2, and IT-1) to gauge different countries’ progress in reducing fine particulate pollution. In 2012, the PRC updated the AQS and set 35 μg/m3 as the annual average PM2.5 standard, equaling WHO's first interim target level of 35 μg/m3. Figure 1 shows that the PM2.5 concentration of all major cities in the PRC in 2016 exceeded the AQG target. Two-thirds did not meet WHO's first interim and the PRC's AQS.

Figure 1. 

Annual Average Fine Particulate Pollution Concentration in Chinese Cities at the Prefecture Level and Above, 2016

AQG = World Health Organization's Air Quality Guidelines, AQS = ambient air quality standard, IT = World Health Organization's Air Quality Interim Target, PM2.5 = particulates with aerodynamic diameter ≤2.5 μm, PRC = People's Republic of China.

Figure 1. 

Annual Average Fine Particulate Pollution Concentration in Chinese Cities at the Prefecture Level and Above, 2016

AQG = World Health Organization's Air Quality Guidelines, AQS = ambient air quality standard, IT = World Health Organization's Air Quality Interim Target, PM2.5 = particulates with aerodynamic diameter ≤2.5 μm, PRC = People's Republic of China.

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In sum, reducing PM2.5 concentration in the PRC's cities will inevitably be a difficult and long-term process. This is evident from the severe and complex nature of the pollution itself, the policy dynamics of the Action Plan, and the large gap between current pollution levels and WHO's interim targets. We examine the economic value of various PM2.5 concentration or reduction scenarios to provide a welfare perspective on this long-term PM2.5 control process. As defined earlier, we define the values as the total cost, total benefit of WHO's AQG (TBAQG) and three interim targets (TBIT-1, TBIT-2, and TBIT-3), and the benefit of a uniform 10 μg/m3 decrease in fine particulate pollution concentration (UB10). As discussed in the next sections, total cost, together with the physical estimates of premature deaths, can represent a major part of the welfare loss attributable to fine particulate pollution across cities. The results of UB10 answer important questions such as how significant is the benefit of near-term achievable PM2.5 control, and why relatively clean cities should further reduce their fine particulate pollution. The relative sizes of the total cost, total benefit, and the benefit of a uniform 10 μg/m3 decrease in fine particulate pollution concentration have implications for setting targets and choosing appropriate speeds for PM2.5 pollution reduction across cities.

In this section, we build an estimation framework using an integrated exposure-response (IER) model and parameters suitable for the PRC context. We first note that, regardless of the estimation scales used, the economic value of the mortality (or mortality changes) attributable to PM2.5 will eventually be composed of the following four sets of information: (i) the exposed population; (ii) the baseline mortality rate; (iii) the CR relationship, which is a monotonic increasing function of PM2.5 concentration with its slope usually called the CR coefficient; and (iv) the value of a statistical life (VSL), which represents the willingness to pay for mortality risk reduction aggregated over the affected population. As discussed below, holding other factors unchanged, the economic estimate will grow with an increase in any of the four factors.

A.  Premature Deaths Attributable to 1 Year of Exposure to Fine Particulate Pollution

In epidemiological studies of the health effects of ambient air pollution, the relationship between PM2.5 exposure and mortality is characterized by the CR function. The conventional view is that the CR function is a linear function of PM2.5 concentration, ci, with a slope defined as the CR coefficient (Dockery et al. 1993, Pope et al. 2002). Pooled estimates from meta analyses, such as the one by Hoek et al. (2013), suggest the CR coefficient is a 6% (95% confidence interval from 4% to 8%) increase in all-cause mortality per 10 μg/m3 increase in PM2.5 concentration. The constant CR coefficient assumption has been applied in many developed countries where PM2.5 concentration generally ranges below 30 μg/m3. Accordingly, the relative risk (RR) for premature death at PM2.5 concentration ci can be written as
RR(ci)=1+CRcoefficient*(ci-c0)
(1)
where c0 is the threshold concentration that causes health impacts.

Recent evidence suggests that the CR function is nonlinear (concave), and the CR coefficient is lower when the PM2.5 concentration is higher (Pope et al. 2011, Burnett et al. 2014). This implies that using the constant CR coefficient derived from studies of cleaner developed countries may overestimate the health impacts from fine particulate pollution in places with very high PM2.5 concentrations. To better describe the concave characteristics of the CR function, an IER model was developed by Burnett et al. (2014) and Smith et al. (2014). It has been applied in the Global Burden of Disease project (Lim et al. 2013) and recently in PRC-specific studies (Liu et al. 2016a, Xie et al. 2016a, Xie et al. 2016b, and Mu and Zhang 2015). The IER simulates a set of disease-specific RR curves that cover the whole range of PM2.5 exposure by integrating available evidence for the health impacts of ambient air pollution, indoor air pollution from household solid fuel use, second-hand smoking, and active smoking. It therefore can be used to estimate the PM2.5 mortality risks for cities in the PRC with high concentrations.

The IER describes the RR for several diseases, k, associated with PM2.5 pollution.
RRkci=1+αk1-e-γkci-c0δk,ifci>c01,else
(2)
where k is one of the four major adult diseases associated with PM2.5: ischemic heart disease, chronic obstructive pulmonary disease, stroke, and lung cancer. αk, γk, and δk are the parameters characterizing the shapes of the RRk curve.

The Global Burden of Disease Study (2013) provides the exact RRkci value and its 95% confidence interval for disease k at each integer's concentration level for the IER model. Therefore, given city j’s 2016 annual average PM2.5 concentration (ci), we know the corresponding RRkci.

The attribution fraction of the mortality risks of disease k from PM2.5, AFk can be written as
AFk=RRk(ci)-1RRk(ci)
(3)
The disease-specific premature deaths due to the annual exposure to ambient PM2.5 in city i, (Dki), is
Dki=Iki*Pi*AFk
(4)
where Iki is the mortality rate for disease k in city i. Currently, there is no publicly available city-level mortality information for cities in the PRC. We apply the provincial disease-specific mortality estimated by Liu et al. (2016a) to all the cities in the same province. Pi is the population of city i.

B.  Total Cost of Mortality Attributable to 1 Year of Exposure to Fine Particulate Pollution

The premature deaths from PM2.5 are monetized using the VSL estimates for the PRC. VSL is the marginal rate of substitution between income and micro mortality risk reduction (Hammitt 2000) and is nonconstant across income groups and risk contexts (Cameron and DeShazo 2013). It is obtained by aggregating individuals’ willingness to pay for mortality risk reductions over the affected population. Therefore, the higher the income level of the affected population, the higher their willingness to pay and VSL. Meta analyses of VSL empirical estimates show that it increases with the income of the sampled population and decreases with the level of mortality risk reduction in the research (Viscusi and Aldy 2003, Lindhjem et al. 2011). As a result, Cameron (2010), among others, advocates not using a one-size-fits-all VSL for populations across sites and regulatory contexts, and proposes adjusting it to fit the population's income level and a study's risk settings.

Empirical VSL estimates for the PRC are limited, and the data are more than 10 years out-of-date. Reviews of these studies are available in Huang, Andersson, and Zhang (2017); and Jin (2017). Due to the large differences in income and health risks between developed countries and the PRC, and the changes over the last decade, applying VSL values from United States-based or old PRC studies to current PRC data sets using benefit transfer methods would incur large uncertainties. Motivated by this gap, Jin (2017) designed a state-of-the-art discrete choice experiment (DCE) that incorporated the newest IER evidence on the health impacts of air pollution and constructed risk reduction scenarios that are realistic in the PRC setting. Based on online DCE surveys implemented in September 2016 on a representative sample of over 1,000 Beijing citizens, Jin reported the VSL for air pollution mortality impacts in Beijing to be CNY3 million (95% confidence interval from CNY2.2 million to CNY5 million, in 2016 prices).11 More detailed information of this DCE survey, econometric analysis, and results are available in Jin (2017).12 This VSL estimate of Beijing's air pollution fits this study's research objective, as it is up-to-date and the risk contexts in the two studies are the same.

As Jin (2017) did not find significant differences in the VSLs of the major adult diseases associated with PM2.5, we use her base estimate of CNY3 million as the VSLBeijing,airpollution to monetize the premature deaths Dki. We then adjust the VSLBeijing,airpollution to every city i as follows:
VSLi=VSLBeijing,airpollution*YBeijingYie
(5)
where YBeijing and Yi are, respectively, urban per capita disposable income in Beijing and in city i, and e is the income elasticity of VSL, which in this study is assumed to be 1.13
The total cost of the premature deaths for city i (TCi) can be written as
TCici=VSLi*kDki
(6)
Finally, combining equation (6) with equation (1) to equation (5), the total cost of premature deaths in city i from 1 year of exposure to the average annual PM2.5 concentration ci is
TCici=VSLBeijing,airpollution*YBeijingYie*PikIki*RRkci-1RRkci
(7)

C.  Benefit of Mortality Reduction from a Uniform 10 μg/m3 Decrease in PM2.5 Concentration and from Meeting WHO AQG and Interim Targets

In this study, the benefit of mortality reduction from a uniform 10 μg/m3 decrease in PM2.5 concentration in city i is defined as the TCici in equation (7) minus TCici-10, i.e., the total cost in the same year with a counterfactual 10 μg/m3 reduction in PM2.5 concentration. The interpretation is as follows: if city i realizes a 10 μg/m3 reduction in annual average PM2.5 concentration, and everything else stays at the 2016 level, then the benefit of prevented premature deaths caused by this specific reduction in PM2.5 concentration equals the following:
UB10,ici=TCici-TCici-10
(8)

We define UB10 as based on a 10 μg/m3 decrease, rather than on a 1 μg/m3 decrease of ci because a 10 μg/m3 change is more policy relevant. A stable and significant reduction in the PM2.5 concentration of 10 μg/m3 could in most cases only be achieved through policy, whereas a 1 μg/m3 change may occur due to natural factors such as meteorological fluctuations. Furthermore, the PRC's Action Plan sets air quality improvement goals based on percentage changes that, if transformed into concentration changes, are on a 10 μg/m3 order of magnitude. Therefore, using a 10 μg/m3 change in our estimations provides ready-to-use references for local policy economic evaluations.

Likewise, the benefit of reductions in PM2.5 mortality achieved by meeting different air quality targets, TBAQG, TBIT-1, TBIT-2, and TBIT-3, are defined as
TBt,ici=TCici-TCict,ifci>ct0,else
(9)
where t is one of the four air quality targets and ct is each target's corresponding concentration level, which are WHO's AQG (10 μg/m3) and interim targets, IT-3 (15 μg/m3), IT-2 (25 μg/m3), and IT-1 (35 μg/m3).

A.  Estimated Premature Deaths

Our first set of results is for the estimated premature deaths attributable to 1 year of exposure to ambient PM2.5 in 2016 in the major cities in the PRC. Table 1 reports the estimated total reductions and disease-specific reductions. It also compares our results with those of the newest estimates given in the Global Burden of Disease Study (2015) and in Liu et al. (2016a). In the latter's analysis, the premature deaths are also calculated based on the IER model, but for the entire population of the PRC.

Table 1. 
Premature Deaths Attributable to Ambient PM2.5—Comparison of National and Urban Estimates
Estimates and Calculations GBD 2015 (a) This Study (b) Urban/National Ratio (b/a) 
Research areas in the People's Republic of China nationwide urban areas — 
Population affected (million) [A] 1,370 446 0.3 
Total deaths except from injuries (million) [B] 8.6 2.7 0.3 
Premature deaths due to PM2.5 (million) [C] 1.1 0.7 0.6 
Rate of disease-related death (per 100,000) [B]/[A] 630 620 0.9 
Percent attributable to PM2.5 [C]/[B] 12.8% 25.0% 1.9 
Premature Deaths Due to PM2.5 by Disease GBD 2015 Liu et al. 2016  This Study 
Stroke 322,228 688,000 349,139 
Ischemic heart disease 291,764 381,900 185,860 
Lung cancer 145,985 129,400 58,343 
Chronic obstructive pulmonary disease 281,703 168,100 76,084 
Estimates and Calculations GBD 2015 (a) This Study (b) Urban/National Ratio (b/a) 
Research areas in the People's Republic of China nationwide urban areas — 
Population affected (million) [A] 1,370 446 0.3 
Total deaths except from injuries (million) [B] 8.6 2.7 0.3 
Premature deaths due to PM2.5 (million) [C] 1.1 0.7 0.6 
Rate of disease-related death (per 100,000) [B]/[A] 630 620 0.9 
Percent attributable to PM2.5 [C]/[B] 12.8% 25.0% 1.9 
Premature Deaths Due to PM2.5 by Disease GBD 2015 Liu et al. 2016  This Study 
Stroke 322,228 688,000 349,139 
Ischemic heart disease 291,764 381,900 185,860 
Lung cancer 145,985 129,400 58,343 
Chronic obstructive pulmonary disease 281,703 168,100 76,084 

GBD = Global Burden of Disease Study, PM2.5 = particulates with aerodynamic diameter ≤2.5 μm.

Source: Authors’ calculations.

Our results, as shown in Table 1, suggest that 0.67 million premature deaths are attributable to ambient PM2.5 in the urban areas of major cities in the PRC cities in the 2016 data set. Although the urban-registered population in our study area is 0.45 billion, which is only 33% of the 1.37 billion people living in the PRC, the premature deaths due to ambient fine particulate pollution account for 60% of the deaths nationally. The rate of disease-related death for urban residents is almost the same as that of the whole population, suggesting that although there is no difference between the baseline mortality risks of urban residents and the national average, the percentage of deaths attributable to ambient PM2.5 in urban areas is almost twice the national average. These results indicate that urban residents face considerably higher mortality risks associated with ambient fine particulate pollution than the general population.

The lower part of Table 1 compares disease-specific premature deaths. Stroke accounts for the majority of premature deaths, followed by ischemic heart disease; chronic obstructive pulmonary disease and lung cancer are less common. This is consistent with the fact that the baseline mortality risks for stroke and ischemic heart disease are higher. The relative magnitude of the four types of disease-specific PM2.5 mortalities in our study are similar to the patterns in nationwide estimates.

Cities with higher estimates of premature deaths are those with larger populations and higher PM2.5 concentrations. The magnitudes and distribution of our results are in accordance with recent grid-based studies such as Liu et al. (2016a) and Xie et al. (2016a).

B.  Total Cost of PM2.5 Mortality, Benefit of a Uniform 10 μg/m3 Decrease in PM2.5 Concentration, and Benefit of Meeting WHO AQG and Interim Targets

In the second set of results, we determine welfare outcomes by analyzing the monetary terms of different PM2.5 mortality impacts. We begin with the total cost estimates; that is, the total cost of PM2.5 mortality. It represents a majority of the annual social welfare loss if ambient fine particulate pollution remains unchanged. In other words, total cost is the theoretical maximum benefit that could result from reducing the current PM2.5 concentration to zero. In major cities in the PRC in 2016, it aggregated to about CNY1,172 billion. The spatial distribution of total cost is shown in Figure 2. Megacities in the Beijing–Tianjin–Hebei and Yangtze River Delta regions, as well as Chengdu and Chongqing in the central PRC, have the highest total cost. Income, population, and PM2.5 concentration are all high in these cities, pulling up their total cost estimates. In the highly developed Pearl River Delta region, although PM2.5 concentration is relatively low, populated cities such as Guangzhou and Shenzhen have very high total cost.

Figure 2. 

Total Cost of Mortality Attributable to 1 Year of Exposure to PM2.5, 2016 (CNY million)

CNY = yuan.

Figure 2. 

Total Cost of Mortality Attributable to 1 Year of Exposure to PM2.5, 2016 (CNY million)

CNY = yuan.

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We then consider UB10, that is, the benefit of mortality reduction from a uniform 10 μg/m3 decrease in PM2.5 concentration in 2016 in each city. It should be noted that for most of the PRC's cities with currently high PM2.5 concentration levels, UB10 measures the near-term and achievable benefits of implementing effective fine particulate pollution control measures. In contrast, for cities with a very low PM2.5 concentration, a further decline of 10 μg/m3 is a big reduction and may be difficult to achieve. The total UB10 of all cities in 2016 was about CNY141 billion. Importantly, the spatial distribution of the UB10, shown in Figure 3, is very different from that of the total cost, shown in Figure 2. Beijing, Tianjin, Chongqing, and provincial capitals with high PM2.5 concentrations such as Chengdu and Shenyang have high UB10 values. However, cities with medium and low PM2.5 concentrations, high incomes, and high populations, such as those along the eastern and southern coastline, also have very high UB10.

Figure 3. 

The Benefits of Mortality Reduction from a Uniform 10 μg/m3 Decrease in PM2.5 Concentration Reduction (CNY million)

CNY = yuan.

Figure 3. 

The Benefits of Mortality Reduction from a Uniform 10 μg/m3 Decrease in PM2.5 Concentration Reduction (CNY million)

CNY = yuan.

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Next, we compare the values for total cost with total benefit, which include the benefit of a reduction of mortality achieved by meeting different air quality targets and a uniform 10 μg/m3 decrease in fine particulate pollution concentration for five representative cities and for all cities (Figure 4). Intuitively, it seems that more stringent air quality targets will have greater benefits. However, a disproportionately increasing trend can be seen from TBIT-1 to TBIT-3 and finally TBAQG. This is due to the concavity of the CR relationship between PM2.5 concentration and mortality. The current PRC's AQS of 35 μg/m3, which is equal to WHO's first interim target, represents a huge reduction in PM2.5 concentration for some cities, but the associated benefit (TBIT-1) is very small (less than one-third of the total cost). Cities will achieve a much higher total benefit (TBIT-3) (more than one-half of the total cost) if the annual PM2.5 concentration is lower than WHO's third interim target level of 15 μg/m3. When cities meet WHO's AQG, the TBAQG is about 80% of the total cost. Figure 4 illustrates the welfare implications of the uniform 10 μg/m3 decrease in fine particulate pollution concentration for different cities. For dirty cities such as Beijing and Urumqi, UB10 is much lower than the total benefit, whereas for clean cities such as Guangzhou and Kunming, it corresponds to a significant pollution reduction and is therefore comparable to the total cost.

Figure 4. 

Total Cost of PM2.5 Mortality, Total Benefits of Meeting Different Air Quality Standards, and Benefits of a Uniform 10 μg/m3 Decrease in PM2.5 Concentration

AQG = World Health Organization's Air Quality Guidelines, CNY = yuan, IT = World Health Organization's Air Quality Interim Target, PM2.5 = particulates with aerodynamic diameter ≤2.5 μm, TB = benefit of mortality reduction achieved by meeting certain air quality targets, TC = total cost of PM2.5 mortality, UB10 = benefit of a uniform 10 μg/m3 decrease in PM2.5 concentration.

Notes: The scales are different in the vertical axes of the left and right panels. $1 = CNY6.64 (2016 average prices).

Figure 4. 

Total Cost of PM2.5 Mortality, Total Benefits of Meeting Different Air Quality Standards, and Benefits of a Uniform 10 μg/m3 Decrease in PM2.5 Concentration

AQG = World Health Organization's Air Quality Guidelines, CNY = yuan, IT = World Health Organization's Air Quality Interim Target, PM2.5 = particulates with aerodynamic diameter ≤2.5 μm, TB = benefit of mortality reduction achieved by meeting certain air quality targets, TC = total cost of PM2.5 mortality, UB10 = benefit of a uniform 10 μg/m3 decrease in PM2.5 concentration.

Notes: The scales are different in the vertical axes of the left and right panels. $1 = CNY6.64 (2016 average prices).

Close modal

In Table 2, we list the 20 cities with the highest estimates of attributable premature deaths along with their corresponding total cost, total benefit of WHO's third interim target (15 μg/m3), and the benefit of a uniform 10 μg/m3 decrease in PM2.5 concentration. The cities’ rankings of total cost and total benefit of WHO's third interim target vary slightly from their ranking of premature deaths, but the ranking of UB10 is totally different from the other three measures. In fact, among the cities with the most deaths, cleaner and dirtier cities swap rankings for the UB10, and many new cities that are generally cleaner and wealthier appear on the UB10’s top 20 list.

Table 2. 
Top 20 Cities for Attributable Premature Deaths, TC, TBIT-3 (15 μg/m3), and UB10
Attributable Premature Deaths Total Cost Total Benefit IT-3 (15 μg/m3UB10 
Top 20 Cities Deaths Top 20 Cities CNY billion Top 20 Cities CNY billion Top 20 Cities CNY million 
Chongqing 36,113 Chongqing 56.0 Chongqing 36.7 Shanghai 5,837 
Tianjin 17,032 Beijing 51.1 Beijing 35.0 Guangzhou 5,190 
Beijing 17,030 Shanghai 46.2 Shanghai 28.6 Chongqing 4,858 
Shanghai 15,302 Tianjin 33.1 Tianjin 22.5 Nanjing 3,494 
Chengdu 12,692 Nanjing 29.3 Nanjing 18.6 Shenzhen 3,051 
Nanjing 11,190 Guangzhou 24.7 Chengdu 16.1 Beijing 2,452 
Xi'an 10,803 Chengdu 23.9 Guangzhou 14.0 Hangzhou 2,192 
Guangzhou 9,256 Xi'an 20.2 Xi'an 13.8 Shantou 2,123 
Shijiazhuang 9,021 Hangzhou 19.2 Hangzhou 12.2 Kunming 2,076 
Zhengzhou 8,417 Wuhan 17.0 Wuhan 11.2 Suzhou 2,057 
Wuhan 8,149 Suzhou 16.5 Jinan 10.6 Xiamen 1,912 
Shenyang 7,468 Shenyang 15.3 Suzhou 10.4 Tianjin 1,776 
Harbin 7,406 Jinan 15.2 Shijiazhuang 10.2 Foshan 1,768 
Hangzhou 7,006 Zhengzhou 14.6 Zhengzhou 10.2 Qingdao 1,711 
Tangshan 6,802 Shijiazhuang 14.4 Shenyang 9.9 Chengdu 1,634 
Jinan 6,752 Qingdao 13.0 Changsha 8.4 Fuzhou 1,561 
Xuzhou 6,266 Changsha 12.9 Harbin 8.3 Nanning 1,429 
Baoding 6,107 Harbin 12.9 Tangshan 8.3 Shenyang 1,371 
Suzhou 5,780 Changzhou 12.5 Qingdao 8.2 Putian 1,336 
Qingdao 5,711 Tangshan 12.0 Changzhou 8.1 Wuhan 1,326 
Attributable Premature Deaths Total Cost Total Benefit IT-3 (15 μg/m3UB10 
Top 20 Cities Deaths Top 20 Cities CNY billion Top 20 Cities CNY billion Top 20 Cities CNY million 
Chongqing 36,113 Chongqing 56.0 Chongqing 36.7 Shanghai 5,837 
Tianjin 17,032 Beijing 51.1 Beijing 35.0 Guangzhou 5,190 
Beijing 17,030 Shanghai 46.2 Shanghai 28.6 Chongqing 4,858 
Shanghai 15,302 Tianjin 33.1 Tianjin 22.5 Nanjing 3,494 
Chengdu 12,692 Nanjing 29.3 Nanjing 18.6 Shenzhen 3,051 
Nanjing 11,190 Guangzhou 24.7 Chengdu 16.1 Beijing 2,452 
Xi'an 10,803 Chengdu 23.9 Guangzhou 14.0 Hangzhou 2,192 
Guangzhou 9,256 Xi'an 20.2 Xi'an 13.8 Shantou 2,123 
Shijiazhuang 9,021 Hangzhou 19.2 Hangzhou 12.2 Kunming 2,076 
Zhengzhou 8,417 Wuhan 17.0 Wuhan 11.2 Suzhou 2,057 
Wuhan 8,149 Suzhou 16.5 Jinan 10.6 Xiamen 1,912 
Shenyang 7,468 Shenyang 15.3 Suzhou 10.4 Tianjin 1,776 
Harbin 7,406 Jinan 15.2 Shijiazhuang 10.2 Foshan 1,768 
Hangzhou 7,006 Zhengzhou 14.6 Zhengzhou 10.2 Qingdao 1,711 
Tangshan 6,802 Shijiazhuang 14.4 Shenyang 9.9 Chengdu 1,634 
Jinan 6,752 Qingdao 13.0 Changsha 8.4 Fuzhou 1,561 
Xuzhou 6,266 Changsha 12.9 Harbin 8.3 Nanning 1,429 
Baoding 6,107 Harbin 12.9 Tangshan 8.3 Shenyang 1,371 
Suzhou 5,780 Changzhou 12.5 Qingdao 8.2 Putian 1,336 
Qingdao 5,711 Tangshan 12.0 Changzhou 8.1 Wuhan 1,326 

CNY = yuan, IT = World Health Organization's Air Quality Interim Target, PM2.5 = particulates with aerodynamic diameter ≤2.5 μm, UB10 = benefit of a uniform 10 μg/m3 decrease in PM2.5 concentration.

Notes: Total benefit (TB) refers to the benefit of mortality reduction achieved by meeting certain air quality targets. Total cost refers to the total cost (TC) of PM2.5 mortality. $1 = CNY6.64 (2016 average prices).

Source: Authors’ calculations.

C.  Comparing Cities Using per Capita Welfare Indicators

Our third set of results considers per capita welfare indicators. We use them to remove the influence of huge disparities in cities’ populations on our results. Due to space limitations, we focus on per capita total cost and per capita UB10. Per capita total cost can be interpreted as the economic cost of the annual attributable mortality risk for an average individual in city i in 2016. Per capita UB10 represents the economic benefit of this individual's mortality risk reduction from a 10 μg/m3 decrease in PM2.5 concentration. We also study how income and PM2.5 pollution levels influence individual welfare. Ideally, we would use subsamples of populations with different socioeconomic statuses within and across cities for this purpose. However, due to data limitations, we only approximately examine this issue by looking at the difference between low- and high-income cities. We equally divide the cities into five income groups with the first group having the lowest income and the fifth group having the highest income. Then we plot the per capita total cost and per capita UB10 for these cities against the PM2.5 concentration, as shown in Figures 5 and 6, respectively. The areas of the circles represent the city's population.

Figure 5. 

Per Capita Total Cost for Cities with Different Incomes and PM2.5 Concentrations

CNY = yuan, PM2.5 = particulates with aerodynamic diameter ≤2.5 μm.

Notes: Cities are divided into five equal groups according to urban per capita disposable income, from the 1st (lowest income group) to the 5th (highest income group). Circle size represents the affected population in each city. $1 = CNY6.64 (2016 average prices).

Figure 5. 

Per Capita Total Cost for Cities with Different Incomes and PM2.5 Concentrations

CNY = yuan, PM2.5 = particulates with aerodynamic diameter ≤2.5 μm.

Notes: Cities are divided into five equal groups according to urban per capita disposable income, from the 1st (lowest income group) to the 5th (highest income group). Circle size represents the affected population in each city. $1 = CNY6.64 (2016 average prices).

Close modal
Figure 6. 

Per Capita Benefit of Reduction in Mortality from a 10 μg/m3 Decrease in PM2.5 Concentration for Cities with Different Per Capita Incomes and PM2.5 Concentrations

CNY = yuan, PM2.5 = particulates with aerodynamic diameter ≤2.5 μm.

Notes: Cities are divided into five equal parts according to urban per capita disposable income, from 1st (lowest income group) to 5th (highest income group). Circle size represents the affected population in each city. $1 = CNY6.64 (2016 average prices).

Figure 6. 

Per Capita Benefit of Reduction in Mortality from a 10 μg/m3 Decrease in PM2.5 Concentration for Cities with Different Per Capita Incomes and PM2.5 Concentrations

CNY = yuan, PM2.5 = particulates with aerodynamic diameter ≤2.5 μm.

Notes: Cities are divided into five equal parts according to urban per capita disposable income, from 1st (lowest income group) to 5th (highest income group). Circle size represents the affected population in each city. $1 = CNY6.64 (2016 average prices).

Close modal

For per capita total cost in Figure 5, we see that cities with the highest per capita total cost are high-income cities with PM2.5 concentrations between 40 μg/m3 and 60 μg/m3. Residents in low-income and high PM2.5 concentration cities (see the circles for the first and second income groups with PM2.5 > 60 μg/m3) bear a greater burden in terms of the economic costs of PM2.5 mortality.

Figure 6 shows a clear trend of decreasing per capita UB10 as PM2.5 concentration increases. This is contrary to the conventional understanding that the first unit of pollution reduction has the highest benefit. As indicated in previous analyses, this is due to the concavity of the PM2.5 CR function. In each income group, the per capita UB10 spans a wide range, from about CNY100 to CNY900.

D.  How Do Cities in the People's Republic of China Differ from Cities in Other Countries?

Our last exercise is to compare the results for cities in the PRC with those in other countries. We choose two megacities in developed countries, New York City (population 8.5 million; annual PM2.5 concentration in 2016, 13.5 μg/m3) and Seoul (population 10.3 million; annual PM2.5 concentration in 2016, 27 μg/m3). We also choose Monrovia (population 1.1 million; annual PM2.5 concentration in 2016, 22 μg/m3), the capital city of Liberia, to represent cities in least developed countries. We perform the same total cost and UB10 estimation process on the three cities. It should be noted that it is a huge step for cities with low PM2.5 concentrations to further reduce them by 10 μg/m3 (e.g., for New York City, the final PM2.5 concentration would be near zero).

Figure 7 shows the estimated per capita total cost for all these cities. The results for the PRC's cities are well below CNY5,000, whereas they are near CNY15,000 for New York City and CNY20,000 for Seoul. Similarly, Figure 8 indicates that the per capita UB10 is much lower in cities in the PRC than in cities in developed countries. The huge differences are mainly due to the concavity of the CR function and the income disparity between cities in the PRC and those in developed countries.

Figure 7. 

Per Capita Total Cost for Cities in the PRC versus Monrovia, New York City, and Seoul

CNY = yuan, PM2.5 = particulates with aerodynamic diameter ≤2.5 μm, PRC = People's Republic of China.

Notes: $1 = CNY6.64 (2016 average prices). Circle size represents the affected population in each city.

Figure 7. 

Per Capita Total Cost for Cities in the PRC versus Monrovia, New York City, and Seoul

CNY = yuan, PM2.5 = particulates with aerodynamic diameter ≤2.5 μm, PRC = People's Republic of China.

Notes: $1 = CNY6.64 (2016 average prices). Circle size represents the affected population in each city.

Close modal
Figure 8. 

Per Capita UB10 for Cities in the PRC versus Monrovia, New York City, and Seoul

CNY = yuan, PM2.5 = particulates with aerodynamic diameter ≤2.5 μm, PRC = People's Republic of China, UB10 = benefit of a uniform 10 μg/m3 decrease in PM2.5 concentration.

Notes: S1 = CNY6.64 (2016 average prices). Circle size represents the affected population in each city.

Figure 8. 

Per Capita UB10 for Cities in the PRC versus Monrovia, New York City, and Seoul

CNY = yuan, PM2.5 = particulates with aerodynamic diameter ≤2.5 μm, PRC = People's Republic of China, UB10 = benefit of a uniform 10 μg/m3 decrease in PM2.5 concentration.

Notes: S1 = CNY6.64 (2016 average prices). Circle size represents the affected population in each city.

Close modal

Figure 9 further explains the difference in the benefit of a uniform 10 μg/m3 decrease in fine particulate pollution concentration between cities. In the right part of Figure 9, we show several typical cities along the per capita UB10 curve, from those with low pollution levels (Shenzhen, Sanya, and Kunming), through middle pollution levels (Guangzhou, Shanghai, and Zhangye), to high pollution levels (Chongqing, Beijing, Urumqi, and Hengshui). These cities also vary significantly in per capita income and population. For example, Beijing and Urumqi have almost the same PM2.5 concentration levels, but Beijing has a higher per capita income than Urumqi; therefore, the per capita UB10 in Beijing is higher. On the left panel of Figure 9, we multiply per capita UB10 by each city's population and get squares representing the size of the UB10. In this figure, the difference in the populations of Beijing and Urumqi results in more widely differing UB10. Similarly, all cities with high PM2.5 concentrations have relatively lower UB10, and cities with middle to high pollution levels, relatively small populations, and low per capita income have the lowest (e.g., Hengshui, Urumqi, and Zhangye). Figure 9 also suggests that when dirty cities reduce their fine particulate pollution enough to enter the lower PM2.5 concentration ranges, the UB10 of a further reduction significantly increases. For example, if Beijing's concentration decreases to the level of Shanghai's, they would have similar UB10 values, and if they both become as clean as Shenzhen, they would have very large values.

Figure 9. 

UB10 for Typical Cities with Different Characteristics

CNY = yuan, PM2.5 = particulates with aerodynamic diameter ≤2.5 μm, UB10 = benefit of a uniform 10 μg/m3 decrease in PM2.5 concentration.

Note: $1 = CNY6.64 (2016 average prices).

Figure 9. 

UB10 for Typical Cities with Different Characteristics

CNY = yuan, PM2.5 = particulates with aerodynamic diameter ≤2.5 μm, UB10 = benefit of a uniform 10 μg/m3 decrease in PM2.5 concentration.

Note: $1 = CNY6.64 (2016 average prices).

Close modal

The first and foremost implication of our results is that when PM2.5 pollution is high, the benefit of controlling it is relatively low. Thus, the PRC and other emerging economies with high levels of fine particulate pollution should accelerate their pollution control processes. Although the optimal PM2.5 control pathway and speed depend on factors such as projections of changes in a country's technology, economy, and population, the implications of the relative size differences of the total benefit, and the increasing trend of UB10 are clear: given compliance costs and assuming steady economic development, the lower the PM2.5 concentration, the more beneficial an extra pollution control effort is to society.

Second, our results highlight the importance of considering the cost effectiveness of PM2.5 control policies. This is especially relevant for cities with high pollution levels. To reach the socially optimal pollution level, an efficient policy requires that the marginal cost of pollution reduction is smaller than the marginal benefit.14 Thus, for cities with a UB10 that is close to the marginal benefit of pollution reduction, policies to reduce high pollution have to be very cost effective. However, in the PRC and many developing countries, the cost of controlling PM2.5 across sites and sectors is unclear. Sound economic analyses of the available technologies and policy instruments are urgently needed to enhance the cost effectiveness of PM2.5 control. Furthermore, as fine particulate pollution can be transported over long distances, regional control strategies that combine the efforts of neighboring cities and provinces are essential to cost-effective reductions in PM2.5 concentrations (Wu, Xu, and Zhang 2015).

Third, our estimation scheme can be easily used by the general public and the government. Our approach mainly relies on official statistics and data on PM2.5 annual concentrations. We focus on clearly defined jurisdictional urban areas rather than on cells in grids. As pollution control costs are usually calculated for specific programs and projects within certain jurisdictional areas, our approach helps to unify the cost–benefit analyses of PM2.5 control. This helps local authorities to understand the trade-offs between the cost and benefit for their own administrative areas, and will help neighboring cities to coordinate their pollution control efforts through economic mechanisms (e.g., transfers).

Fourth, our results imply that current policy targets of uniform percentage reductions in regional PM2.5 concentrations do not consider the significant heterogeneity in the welfare impacts of these reductions among cities with different characteristics. We show how income, population, and original fine particulate pollution levels can alter the welfare measures of PM2.5 pollution reduction. More efficient pollution control policies could incorporate these factors and set differential targets for different cities. Our results also demonstrate the importance of equity considerations, which are a critical part of the policy-making process. We show that for low-income and high-pollution cities, the benefit of a uniform 10 μg/m3 decrease in fine particulate pollution concentration is much lower than that for high-income and low-pollution cities. For the former, the sooner they enter the low PM2.5 concentration range, the greater their benefit. However, these less advantaged cities often have more tension between economic growth and pollution control, and are therefore more likely to continue to have high pollution levels. Equity considerations would support the allocation of extra resources to these cities to accelerate the PM2.5 control process.

Air pollution, especially ambient PM2.5, is one of the most pressing challenges for the PRC and many developing countries. It results in millions of premature deaths annually. Although a growing body of literature provides increasingly precise and high-resolution information on fine particulate pollution exposure and its health impacts at the regional, national, and provincial levels, the social welfare implications of PM2.5 mortality in urban areas have remained unclear. Multiplying the estimated premature deaths by monetary values such as VSL does not generate accurate economic results due to the nonlinear concentration response relationship between PM2.5 and mortality, disparities in income and population, and the mismatch between the grid cells commonly seen in the literature and the more policy-relevant jurisdictional areas.

This study develops an accessible estimation scheme to provide welfare implications for PM2.5 mortality in urban areas of the PRC. Based on the integrated exposure-response model, our approach uses publicly available PM2.5 concentration data and city-level socioeconomic statistics to estimate the total cost of PM2.5 mortality, the benefit of a uniform PM2.5 concentration reduction, and the benefit of meeting WHO AQG and interim targets for the urban areas of nearly 300 major PRC cities. The results suggest that in these cities the aggregated total cost of annual PM2.5 mortality for 2016 is about CNY1,172 billion. The average per capita total cost is CNY2,255. The aggregated benefit of mortality reduction resulting from a uniform 10 μg/m3 decrease in PM2.5 concentration (UB10) in these cities is about CNY141 billion for 2016; the per capita UB10 is CNY321. Cities with high incomes and large populations that are located in areas with severe air pollution suffer the highest welfare losses, and the UB10 is lower than for cleaner cities. The benefit of meeting WHO's first interim target is very low relative to the benefit of meeting more stringent air quality targets. As most of the cities in the PRC still have PM2.5 concentrations well above WHO's first interim target of 35 μg/m3, local authorities need to accelerate fine particulate pollution control in a cost-effective manner. Only in this way can greater benefits of PM2.5 mortality reduction be achieved.

Burnett
,
Richard T.
,
C. Arden
Pope
,
Majid
Ezzati
,
Casey
Olives
,
Stephen S.
Lim
,
Sumi
Mehta
,
Hwashin H.
Shin
,
Gitanjali
Singh
,
Bryan
Hubbell
,
Michael
Brauer
,
H. Ross
Anderson
,
Kirk R.
Smith
,
John R.
Balmes
,
Nigel G.
Bruce
,
Haidong
Kan
,
Francine
Laden
,
Annette
Prüss-Ustün
,
Michelle C.
Turner
,
Susan M.
Gapstur
,
W. Ryan
Diver
, and
Aaron
Cohen
.
2014
. “
An Integrated Risk Function for Estimating the Global Burden of Disease Attributable to Ambient Fine Particulate Matter Exposure
.”
Environmental Health Perspective
122
(
4
):
397
403
.
Beijing Municipal Environmental Protection Bureau (MEPB)
.
2014
.
Source Apportionment of PM2.5 in Beijing.
Beijing
.
Beijing Municipal Bureau of Statistics
.
2017
.
Beijing Statistical Yearbook
.
Beijing
:
China Statistics Press
.
Cameron
,
Trudy Ann
.
2010
. “
Euthanizing the Value of a Statistical Life
.”
Review of Environmental Economics and Policy
4
(
2
):
161
78
.
Cameron
,
Trudy Ann
, and
J. R.
DeShazo
.
2013
. “
Demand for Health Risk Reductions.
Journal of Environmental Economics and Management
65
(
1
):
87
109
.
China Environmental Protection Association
.
2016
.
Guangdong and the Pearl River Delta Fulfilled the PM2.5 National Action Goals Two Years Ahead of Schedule.
Beijing
.
China State Council
.
2013
.
Action Plan on Prevention and Control of Air Pollution
(in Chinese)
.
Beijing
.
Chris
,
Cairns
, and
Plantan
Elizabeth
.
2016
.
“Hazy Messaging Framing on Chinese Social Media During Air Pollution Crises
.”
Paper presented at the symposium on Everyday Politics of Digital Life in China
,
Pittsburgh
.
Di
,
Qian
,
Yun
Wang
,
Antonella
Zanobetti
,
Yun
Wang
,
Petros
Koutrakis
,
Christine
Choirat
,
Francesca
Dominici
, and
Joel D.
Schwartz
.
2017
. “
Air Pollution and Mortality in the Medicare Population.
The New England Journal of Medicine
376
(
26
):
2513
22
.
Dockery
,
Douglas
,
C.
Arden Pope
,
Xiping
Xu
,
John
Spengler
,
James
Ware
,
Martha
Fay
,
Benjamin G.
Ferris
, and
Frank
Speizer
.
1993
. “
An Association between Air Pollution and Mortality in Six US Cities
.”
The New England Journal of Medicine
329
(
24
):
1753
59
.
Global Burden of Disease Study
.
2013
.
Global Burden of Disease Study 2010–Ambient Air Pollution Risk Model 1990–2010
.
Seattle
:
Institute for Health Metrics and Evaluation
.
Global Burden of Disease Study
.
2015
. “
GBD 2015 Results
.” https://vizhub.healthdata.org/gbd-compare/ (
accessed November 1, 2016
).
Hammitt
,
James K.
2000
. “
Valuing Mortality Risk: Theory and Practice
.”
Environmental Science & Technology
34
(
8
):
1396
400
.
Hoek
,
Gerard
,
Ranjini M.
Krishnan
,
Rob
Beelen
,
Annette
Peters
,
Bart
Ostro
,
Bert
Brunekreef
, and
Joel
Kaufman
.
2013
. “
Long-Term Air Pollution Exposure and Cardio-Respiratory Mortality: A Review.
Environmental Health
12
(
1
):
12
43
.
Huang
,
Desheng
,
Henrik
Andersson
, and
Shiqiu
Zhang
.
2017
.
“Willingness to Pay to Reduce Health Risks Related to Air Quality: Evidence from a Choice Experiment Survey in Beijing
.” Journal of Environmental Planning and Management. doi: 10.1080/09640568.2017.1389701.
Huang
,
Desheng
,
Jianhua
Xu
, and
Shiqiu
Zhang
.
2012
. “
Valuing the Health Risks of Particulate Air Pollution in the Pearl River Delta, China
.”
Environmental Science & Policy
15
(
1
):
38
47
.
Jiangsu Bureau of Statistics
.
2017
.
Jiangsu Statistical Yearbook
.
Beijing
:
China Statistics Press
.
Jin
,
Yana
.
2017
. “
Valuation of Health Risks with Discrete Choice Experiment and Cost-Benefit Analysis of Air Pollution Control Strategies
.”
PhD dissertation, Peking University
.
Jin
,
Yana
,
Henrik
Andersson
, and
Shiqiu
Zhang
.
2016
. “
Air Pollution Control Policies in China: A Retrospective and Prospects
.”
International Journal of Environmental Research and Public Health
13
(
12
):
1219
41
.
Jin
,
Yana
,
Henrik
Andersson
, and
Shiqiu
Zhang
.
2017
. “
China's Cap on Coal and the Efficiency of Local Interventions: A Benefit-Cost Analysis of Phasing Out Coal in Power Plants and in Households in Beijing
.”
Journal of Benefit-Cost Analysis
8
(
2
):
147
86
.
Lelieveld
,
Johannes
,
John
Evans
,
Mohammed
Fnais
,
Despina
Giannadaki
, and
Andrea
Pozzer
.
2015
. “
The Contribution of Outdoor Air Pollution Sources to Premature Mortality on a Global Scale
.”
Nature
525
(
7569
):
367
71
.
Liang
,
Xuan
,
Shuo
Li
,
Shuyi
Zhang
,
Hui
Huang
, and
Song Xi
Chen
.
2016
. “
PM2.5 Data Reliability, Consistency, and Air Quality Assessment in Five Chinese Cities
.”
Journal of Geophysical Research: Atmospheres
21
(
17
):
10220
36
.
Lim
,
Stephen S.
,
Theo
Vos
,
Abraham D.
Flaxman
,
Goodarz
Danaei
,
Kenji
Shibuya
,
Heather
Adair-Rohani
,
Mohammad A.
AlMazroa
,
Markus
Amann
,
H.
Ross Anderson
, and
Kathryn G.
Andrews
.
2013
. “
A Comparative Risk Assessment of Burden of Disease and Injury Attributable to 67 Risk Factors and Risk Factor Clusters in 21 Regions, 1990–2010: A Systematic Analysis for the Global Burden of Disease Study 2010
.”
The Lancet
380
(
9859
):
2224
60
.
Lindhjem
,
Henrik
,
Ståle
Navrud
,
Nils Axel
Braathen
, and
Vincent
Biausque
.
2011
. “
Valuing Mortality Risk Reductions from Environmental, Transport, and Health Policies: A Global Meta-Analysis of Stated Preference Studies
.”
Risk Analysis
31
(
9
):
1381
407
.
Liu
,
Jun
,
Yiqun
Han
,
Xiao
Tang
,
Jiang
Zhu
, and
Tong
Zhu
.
2016a
. “
Estimating Adult Mortality Attributable to PM2.5 Exposure in China with Assimilated PM2.5 Concentrations Based on a Ground Monitoring Network
.”
Science of the Total Environment
568
:
1253
62
.
Liu
,
Jun
,
Denise L.
Mauzerall
,
Qi
Chen
,
Qiang
Zhang
,
Yu
Song
,
Wei
Peng
,
Zbigniew
Klimont
,
Xinghua
Qiu
,
Shiqiu
Zhang
,
Min
Hu
,
Weili
Lin
,
Kirk R.
Smith
, and
Tong
Zhu
.
2016b
. “
Air Pollutant Emissions from Chinese Households: A Major and Underappreciated Ambient Pollution Source
.”
Proceedings of the National Academy of Sciences
113
(
28
):
7756
61
.
Liu
,
Yiman
.
2015
.
“Revising the Law on the Prevention and Control of Atmospheric Pollution: Why Experts Propose a Major Revision?
” (
in Chinese
).
Southern Metropolis Daily
.
Mu
,
Quan
, and
Shi Qiu
Zhang
.
2013
. “
The Economic Cost Evaluation of Haze Events in China
.”
China Environmental Science
11
:
2087
94
.
Mu
,
Quan
, and
Shi Qiu
Zhang
.
2015
. “
Assessment of the Trend of Heavy PM2.5 Pollution Days and Economic Loss of Health Effects During 2001–2013
.”
Acta Scientiarum Naturalium Universitatis Pekinensis
4
:
694
706
.
Pope
,
C. Arden
,
Richard T.
Burnett
,
Michelle C.
Turner
,
Aaron
Cohen
,
Daniel
Krewski
,
Michael
Jerrett
,
Susan M.
Gapstur
, and
Michael J.
Thun
.
2011
. “
Lung Cancer and Cardiovascular Disease Mortality Associated with Ambient Air Pollution and Cigarette Smoke: Shape of the Exposure–Response Relationships
.”
Environmental Health Perspectives
119
(
11
):
1616
21
.
Pope
,
C. Arden
,
Richard T.
Burnett
,
Michael J.
Thun
,
Eugenia E.
Calle
,
Daniel
Krewski
,
Kazuhiko
Ito
, and
George D.
Thurston
.
2002
. “
Lung Cancer, Cardiopulmonary Mortality, and Long-Term Exposure to Fine Particulate Air Pollution
.”
Journal of the American Medical Association
287
(
9
):
1132
41
.
Pope
,
C. Arden
,
Maureen
Cropper
,
Jay
Coggins
, and
Aaron
Cohen
.
2015
. “
Health Benefits of Air Pollution Abatement Policy: Role of the Shape of the Concentration–Response Function
.”
Journal of the Air and Waste Management Association
65
(
5
):
516
22
.
Qingyue Open Environmental Data Center
.
2016
. https://data.epmap.org.
Shanghai Municipal Environmental Protection Bureau (MEPB)
.
2015
.
Source Apportionment of PM2.5 in Shanghai
.
Shanghai
.
Shao
,
Min
,
Xiaoyan
Tang
,
Yuanhang
Zhang
, and
Wenjun
Li
.
2006
. “
City Clusters in China: Air and Surface Water Pollution
.”
Frontiers in Ecology and the Environment
4
(
7
):
353
61
.
Smith
,
Kirk R.
,
Nigel
Bruce
,
Kalpana
Balakrishnan
,
Heather
Adair-Rohani
,
John
Balmes
,
Zoë
Chafe
,
Mukesh
Dherani
,
H.
Dean Hosgood
,
Sumi
Mehta
,
Daniel
Pope
, and
Eva
Rehfuess
.
2014
. “
Millions Dead: How Do We Know and What Does It Mean? Methods Used in the Comparative Risk Assessment of Household Air Pollution
.”
Annual Review of Public Health
35
(
1
):
185
206
.
Tsinghua University
.
2006
.
Research on Different Sectors and Sources Regulations' Contribution To Air Pollution Abatement in Beijing
(
in Chinese
).
Tsinghua University and Clean Air Alliance of China
.
2014
.
Policy Evaluation Based on the National Ten Actions of PM2.5 Pollution Prevention and Control for the Beijing–Tianjin–Hebei Region
. http://en.cleanairchina.org/product/6659.html.
United States Environmental Protection Agency
.
2006
.
Regulatory Impact Analysis of National Ambient Air Quality Standards for Particle Pollution
.
Washington, DC
.
United States Environmental Protection Agency
2011
.
The Benefits and Costs of the Clean Air Act from 1990 to 2020: Summary Report
.
Washington, DC
.
Viscusi
,
W. Kip
, and
Joseph E.
Aldy
.
2003
. “
The Value of a Statistical Life: A Critical Review of Market Estimates Throughout the World
.”
Journal of Risk and Uncertainty
27
(
1
):
5
76
.
Wang
,
Zhifa
.
2017
.
Huge Investment and High Operation Cost are Two Major Obstacles of Reducing PM2.5 by “Coal-to-Electricity” Measures.
http://www.thepaper.cn/newsDetail_forward_1790920.
World Health Organization (WHO)
.
2005
.
Air Quality Guidelines–Global Update 2005
.
Geneva
.
World Bank
.
2007
.
Cost of Pollution in China. Economic Estimates of Physical Damages.
Washington, DC
.
World Bank
.
2016
.
The Cost of Air Pollution: Strengthening the Economic Case for Action
.
Washington, DC
.
Wu
,
Dan
,
Yuan
Xu
, and
Shiqiu
Zhang
.
2015
. “
Will Joint Regional Air Pollution Control be More Cost-Effective? An Empirical Study of China's Beijing–Tianjin–Hebei Region
.”
Journal of Environmental Management
149
:
27
36
.
Xie
,
Rong
,
Clive E.
Sabel
,
Xi
Lu
,
Weimo
Zhu
,
Haidong
Kan
,
Chris P.
Nielsen
, and
Haikun
Wang
.
2016b
. “
Long-Term Trend and Spatial Pattern of PM2.5-Induced Premature Mortality in China
.”
Environment International
97
:
180
86
.
Xie
,
Yang
,
Hancheng
Dai
,
Huijuan
Dong
,
Tatsuya
Hanaoka
, and
Toshihiko
Masui
.
2016a
. “
Economic Impacts from PM2.5 Pollution-Related Health Effects in China: A Provincial-Level Analysis
.”
Environmental Science and Technology
50
(
9
):
4836
43
.
Xu
,
Jianhua
,
Xuesong
Wang
, and
Shiqiu
Zhang
.
2013
. “
Risk-Based Air Pollutants Management at Regional Levels
.”
Environmental Science and Policy
25
:
167
75
.
Young
,
Oran R.
,
Dan
Guttman
,
Ye
Qi
,
Kris
Bachus
,
David
Belis
,
Hongguang
Cheng
,
Alvin
Lin
,
Jeremy
Schreifels
,
Sarah Van
Eynde
,
Yahua
Wang
,
Liang
Wu
,
Yilong
Yan
,
An
Yu
,
Durwood
Zaelke
,
Bing
Zhang
,
Shiqiu
Zhang
,
Xiaofan
Zhao
, and
Xufeng
Zhu
.
2015
. “
Institutionalized Governance Processes: Comparing Environmental Problem Solving in China and the United States
.”
Global Environmental Change
31
:
163
73
.
1

Cities in the PRC cover both urban and rural areas. There are 653 cities, including 292 cities at the prefecture level and above, and 361 county-level cities. These two types of cities have used different statistical indicator systems since 1997, and some of these indicators are not comparable. Therefore, the data for the two types of cities are presented separately in the statistical yearbooks. Cities at the prefecture level and above provide statistical information for the total city and for urban areas (districts in the city), whereas county-level cities only have information for the total city. In this study, we focus on the urban areas of the 292 cities at the prefecture level and above.

2

The total cost of mortality attributable to PM2.5 is only one important part of the total cost of fine particulate pollution; the latter also includes many other elements such as morbidity costs, citizens’ defensive and adaptive expenditures, productivity losses, decrease in visibility, and damage to materials and crops.

3

IT-1, IT-2, and IT-3 refer to WHO targets of PM2.5 concentration meeting 35 μg/m3, 25 μg/m3, and 15 μg/m3, respectively.

4

As a result, these papers invariably aggregate the cells at the provincial level and then use provincial average socioeconomic data to discuss the likely socioeconomic factors.

5

In our analysis, urban areas meet the definition of shi xia qu, a statistical indicator in the PRC, which refer to city-governed districts, city-controlled districts, or municipal districts that are subdivisions of prefecture-level or larger cities.

6

$1 = CNY6.64 at the 2016 average exchange rate.

7

In cities in the PRC there are both citizens with urban hukou (household registration) and unregistered rural–urban migrants who live in cities without an urban hukou. In this paper, we focus on the urban-registered population. Examining the environmental health impacts of the rural–urban migrants are of importance and a city-level analysis would rely on nonpublicly available data sources, which we direct to future studies.

9

See, for example, Beijing Municipal Bureau of Statistics (2017) and Jiangsu Bureau of Statistics (2017).

10

Although adverse health effects are still observable below this concentration (Di et al. 2017), the 10 μg/m3 level is recommended as the long-term guideline value as it is only slightly higher than the environmental background PM2.5 and has been shown to be achievable in large urban areas in highly developed countries (WHO 2005).

11

As the VSL enters estimation in this study as a multiplier, its absolute size only ``shifts'' the welfare indicators to higher or lower levels. Our conclusions remain.

12

The full survey questionnaire (in English) is available at yanajin.weebly.com.

13

Most studies use per capita GDP to adjust the VSL across sites (e.g., Huang, Xu, and Zhang 2012; and Mu and Zhang 2013). We improve this by using per capita disposable income (Hammitt 2000).

14

This is not necessary in a dynamic efficiency perspective. Dynamic efficiency favors the policy that maximizes the sum of the present value of the net benefit for each period.

 
Appendix: List of Technical Terms
AF attribution fraction of the mortality risks of a disease 
CR function concentration response function 
IT-1 interim target of PM2.5 concentration meeting 35 μg/m3 
IT-2 interim target of PM2.5 concentration meeting 25 μg/m3 
IT-3 interim target of PM2.5 concentration meeting 15 μg/m3 
IT air quality interim target of the World Health Organization 
PM2.5 particulates with aerodynamic diameter ≤2.5 micrometers 
PM2.5 mortality mortality attributable to ambient PM2.5 
PM10 particulates with aerodynamic diameter ≤10 micrometers 
RR relative risk 
TBAQG the benefit of mortality reduction achieved by meeting the World Health Organization's air quality guidelines 
TBIT-1, TBIT-2, and TBIT-3 the benefit of mortality reduction achieved by meeting the World Health Organization's interim targets 1, 2, and 3, respectively. 
TC total cost of PM2.5 mortality 
UB10 the benefit of mortality reduction achieved by a uniform 10 μg/m3 decrease in PM2.5 concentration 
VSL value of a statistical life 
μg/m3 micrograms per cubic meter 
AF attribution fraction of the mortality risks of a disease 
CR function concentration response function 
IT-1 interim target of PM2.5 concentration meeting 35 μg/m3 
IT-2 interim target of PM2.5 concentration meeting 25 μg/m3 
IT-3 interim target of PM2.5 concentration meeting 15 μg/m3 
IT air quality interim target of the World Health Organization 
PM2.5 particulates with aerodynamic diameter ≤2.5 micrometers 
PM2.5 mortality mortality attributable to ambient PM2.5 
PM10 particulates with aerodynamic diameter ≤10 micrometers 
RR relative risk 
TBAQG the benefit of mortality reduction achieved by meeting the World Health Organization's air quality guidelines 
TBIT-1, TBIT-2, and TBIT-3 the benefit of mortality reduction achieved by meeting the World Health Organization's interim targets 1, 2, and 3, respectively. 
TC total cost of PM2.5 mortality 
UB10 the benefit of mortality reduction achieved by a uniform 10 μg/m3 decrease in PM2.5 concentration 
VSL value of a statistical life 
μg/m3 micrograms per cubic meter 

Source: Authors’ compilation.

Author notes

*

We thank the participants at the 2017 CAERE Conference held at Renmin University and the participants at the Asian Development Review Conference in Seoul in August 2017, the managing editor, and two anonymous referees for helpful comments and suggestions. We are grateful to Yuhan Hu for excellent data management. This study was also supported by the National Natural Science Fund of China (41501600) and the Special Fund of State Key Joint Laboratory of Environment Simulation and Pollution Control (18K02ESPCP). ADB recognizes “China” as the People's Republic of China. The usual disclaimer applies.

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