Urbanization, Energy, and Air Pollution in China: The Challenges Ahead: Proceedings of a Symposium (2004)

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HAIDONG KAN and BINGHENG CHEN

Department of Environmental Health

School of Public Health

Fudan University

CHANGHONG CHEN

Shanghai Academy of Environmental Science

To investigate the potential impact of ambient air pollution on public health and the economy in Shanghai, we estimated exposure levels of the general population under various planned energy scenarios and then assessed potential health impacts using concentration-response functions derived from available epidemiologic studies. We then estimated the corresponding economic values of the health effects based on the unit values of the health outcomes. The results showed that ambient air pollution could have a significant impact on the health of Shanghai residents, both in physical and monetary terms. Compared with the base-case (BC) scenario, implementation of various planned energy scenarios could prevent many premature deaths (from 608 to 5,144 in 2010 and from 1,189 to10,462 in 2020) and could substantially reduce the number of pollution-related diseases. The expected economic benefits vary widely depending on the underlying assumptions. Thus, energy and environmental policy could not only reduce air pollution and improve air quality, but could also protect public health and benefit the economy.

Energy and related health issues are of growing concern worldwide. Fossil fuels, the primary sources of energy, are also the greatest sources of ambient air pollution—nitrogen oxides (NO_x), sulfur dioxide (SO₂), dust, soot, smoke, and other suspended fine particulate matter (PM). These pollutants can lead to serious public health problems, including asthma, irritation of the lungs, bronchitis, pneumonia, decreased resistance to respiratory infections, and premature death. The burning of fossil fuels is also the major source of carbon dioxide (CO₂) emissions, a primary contributor to global warming.

In China, a developing country, coal has been the primary energy source.

Rapid economic growth has been accompanied by a rapid increase in the demand for energy, and the relatively inefficient energy technology currently used in China has caused high pollution emissions. However, China has been making great efforts to save energy, optimize its energy structure, and increase energy efficiency to balance energy consumption and environmental needs. As a result of these efforts, the increase in energy consumption has been lower than the increase in gross domestic product (GDP) in China during the past decade.

Clearly, energy-policy decisions today will have a significant impact on future air pollution levels and public health. In the present study, we estimate the public health impact of ambient air pollution under various energy scenarios in Shanghai, one of the fastest growing urban areas in China, and place monetary values on the estimated health effects.

The MARKAL (MARKet ALlocation) optimization model was used to estimate pollutant emissions under various energy scenarios in Shanghai. MARKAL is a dynamic, linear programming model that optimizes a technology-rich network representation of an energy system. MARKAL models the economy of a region as a system, represented by processes and physical and monetary flows among those processes. Details about the application of MARKAL for energy and environmental policies in Shanghai have been discussed elsewhere (Gielen and Chen, 2001).

The energy scenarios developed for Shanghai can be classified into four

categories: (1) base-case (BC); (2) energy options (i.e., use of efficiency measures [eff] or natural gas [NG]); (3) pollution targets (i.e., constraints on SO₂ or NO_x); and (4) control of CO₂ emissions. The details of the scenarios are shown in Table 1.

Based on the principle of transfer matrix, an air-quality model (the Exposure Level Model) was developed to link MARKAL emission scenarios and concentrations of ambient air pollutants. Input to the fundamental matrix was a long-range transport and deposition model (ATMOS model) for SO₂ and primary PM₁₀ (particulates with diameters of 10 mm or less). The ATMOS model is a Lagrangian parcel model with three vertical layers (Calori and Carmichael, 1999).

For the Shanghai project, the ATMOS model provided a 4 × 4 km resolution of the concentration of SO₂ and primary PM₁₀. The total area of Greater Shanghai, 6341 km², was divided into 487 grids. Based on matrix output of the ATMOS model, the Shanghai Exposure Level model was developed in Excel to link MARKAL predictions and provide exposure levels for the analysis of health impacts.

Because strong epidemiologic evidence supports the association of PM₁₀ with adverse health effects, PM₁₀ was selected as a useful indicator of several sources of outdoor air pollution, such as fossil-fuel combustion (Wilson and Spengler, 1996).

Everyone living in Greater Shanghai was considered to be in the exposed population in this analysis. The number of Shanghai residents in each 4 × 4 km grid cell was estimated based on population data from the Shanghai Bureau of Statistics. Combining the PM₁₀ level and population in each grid cell, we estimated exposure levels to outdoor air pollution under various scenarios in 2010 and 2020.

To develop estimates of public health impacts of air pollution, we used concentration-response (C-R) coefficients derived from published, peer-reviewed studies in China and worldwide.

Because most epidemiologic studies linking air pollution and health effects are based on a relative-risk model in the form of a Poisson regression, health effects at a given concentration, C, could be given by the following equation:

(1)

C and C₀ are the PM₁₀ concentrations, and E and E₀ are the corresponding health effects under a specific scenario and the BC scenario, respectively. β is the exposure-response function.

Exposure-response functions (β) link air quality changes and heath outcomes. Our preference was to use C-R functions from Chinese studies whenever they were available. Results in the international peer-reviewed literature were used only when the selected end points could not be found in the Chinese literature. If there were several studies describing a C-R function for the same health end point, we used the pooled estimate to get the mean and 95 percent confidence interval (CI) of the coefficient.

The results of the analysis were given as a comparison of the health effects under a specific scenario with the health effects under the BC scenario in 2010 and 2020, respectively.

The analysis was based mainly on the concept of willingness to pay (WTP); the cost of illness (COI) was used as an alternative for morbidity end points that could not be valued based on the existing literature.

The effect of air pollution on mortality was assessed by using the value of a statistical life (VOSL). The literature on VOSL and WTP to avoid a statistical premature death is mainly from the United States. However, because of our limited time and budget, we used a contingent valuation study (CVM) conducted in Chongqing, China, (Wang, 2001) for the estimate of Shanghai VOSL. In the Chongqing study, Wang reported an average WTP of US$34,750. The marginal effect of increased income on WTP was also reported: with an increase in annual

income of US$145.80, the increase in WTP was US$14,550. Taking the annual income difference in 2001 between Chongqing (US$495.7) and Shanghai (US$1,234.5) into account, we did a conversion based on Chongqing’s coefficient between marginal WTP and income to get an estimate for the VOSL in Shanghai.

Because data for China were not available for some end points of morbidity, we used a value based on the conversion used by the U.S. Environmental Protection Agency. The ratio for conversion was based on the per capita income of U.S. and Shanghai residents, and the income elasticity was assumed to be 1. The COI for the end point of hospital admissions and outpatient visits was calculated from data from Shanghai because no studies based on WTP were available for those end points.

The economic value of a change in the incidence of an adverse health outcome was calculated as the change in incidence (the number of avoidable deaths) multiplied by the unit monetary value (the value of a single case avoided). Because of inherent uncertainties in the health and economic impact assessment, the results were given as a range (mean and 95 percent CI). Because both health outcomes and unit values were distributions rather than constants, we conducted a Monte Carlo simulation to calculate the economic values.

Table 2 summarizes the percentage of the population exposed to different levels of PM₁₀ under various scenarios in 2010 and 2020.

It should be emphasized that the PM₁₀ levels in Table 2 are much lower than the actual concentrations in Shanghai because only PM₁₀ from energy consumption was included. PM₁₀ from other sources, such as natural sources, construction sites, and so forth, was not included.

Table 3 summarizes the PM₁₀ exposure-response coefficients (mean and 95 percent CI) and baseline rates of selected health outcomes. The excess cases in each scenario (compared to the BC scenario) were computed based on the change in exposure levels to PM₁₀ under each scenario, exposure-response functions, and baseline rates for the health outcomes.

The potential health benefits in 2010 and 2020 shown in Table 4 clearly indicate that the choice of energy scenario could have a significant impact on the health of Shanghai residents. Compared with the BC scenario, the implementation of other energy scenarios could prevent 608 to 5,144 PM₁₀-related deaths in 2010 and 1,189 to 10,462 PM₁₀-related deaths in 2020.

Table 5 summarizes the unit values (mean and 95 percent CI) for various end points in 2000 in Shanghai and the specific approach used to derive them. By combining the health benefits and unit values, we computed the economic benefits compared to the BC scenario. Table 6 shows the results for 2010 and 2020.

The link between energy and health must be taken into account in decisions addressing rapid economic growth and sustainable development in Shanghai. This study, which is based on the same approaches used internationally for assessments of environmental impacts, shows that an effective energy and environmental policy will be an important factor in reducing air pollution, improving air quality, and promoting public health.

The quantification of the impact of air pollution on public health is a critical component in environmental policy decisions. Given the gaps in scientific knowledge about the health effects of air pollution and the wide range of uncertainties characterizing many aspects of the process, analyzing the total burden of ambient air pollution on public health is a challenging task.

Our estimates were conservative for three reasons. First, in the present analysis we selected only PM₁₀ as an indicator of outdoor air pollution and probably overlooked adverse health effects from exposure to other air pollutants; thus, we probably underestimated the health effects attributable to total air pollution. Although PM₁₀ may be a good indicator, there is clear evidence that other pollutants, such as ozone, NO_x, and SO₂, have independent health effects. In addition, we did not include synergistic effects among air pollutants or with cofactors, such as pollen and other allergens.

Second, the ATMOS model we used could only deal with primary PM₁₀ and SO₂. Thus, we underestimated the health effects attributable to secondary PM₁₀, such as sulfate and nitrate. Previous studies have shown that ammonium sulfate and nitrate account for substantial proportions of fine particles in Shanghai (Ye et al., 2003).

Third, we focused only on health outcomes that could be quantitatively estimated and then translated into monetary values for further assessment. Some end points, such as subclinical symptoms and decreased pulmonary function, were not included in this analysis, although there is evidence of an association between them and exposure to air pollution. We did not estimate cancer-related

effects linked to exposures to ambient air pollution, although recent studies in the United States have suggested an association (e.g., Pope et al., 2002).

Because some of the exposure-response functions we used in this analysis were not available in Chinese studies, we relied on international studies, conducted mostly in the United States and Western Europe. This raises the question of whether results from a developed country can be transferred to a developing country. For example, Chinese studies generally report lower coefficients for exposure-response relationships between air pollution and adverse health effects than studies in the United States and Europe. This is probably because of differences in levels of air pollution, local population sensitivity, age distribution, and especially air pollutant components. For instance, the composition of the motor vehicle fleet in Western Europe and the United States, where most of the epidemiological studies were performed, and the motor vehicle fleet in China differs substantially. Another major difference is the widespread use of coal in China, which suggests that the air pollution mix also differs substantially.

Ideally, when exposure-response functions from developed countries are applied to other regions, for example, Shanghai, they should be revised to account for local conditions, such as the physical (diameter, etc.) and chemical (components) properties of particulates, the socioeconomic status of local populations, etc. However, no reference data are available for such revisions. Until locally derived exposure-response functions become available, this will probably be the weakest part of an analysis.

Because no valuation study of the health end points associated with air pollution in Shanghai had been done before, we had to estimate values from previous studies of similar changes, a procedure called “benefit transfer” or “value transfer” in economics. Characteristics of the concerned population (e.g., age distribution, income, health status, culture) may have contextual effects on the valuation results. For example, different social and health-insurance systems greatly influence people’s risk perception, which affects the WTP to avoid the risk.

If we had transferred the U.S. VOSL directly to the Shanghai study, after accounting for the income difference between the two sites, the VOSL would have been US$780,000, which is much higher than the VOSL estimated in the Chongqing study. The number would be even higher if we had used purchasing power parity (PPP) as the income definition. Obviously, the estimate used in the Chongqing study is better fitted to the Shanghai study in terms of the economic and social situation. Therefore, we tried to use Chinese studies wherever they were available and attempted to stay on the conservative side with a range of reasonable estimates.

There are also inherent uncertainties in transferring values from other study sites, whether in China or elsewhere. Therefore, we strongly suggest that a WTP study for the avoidance of air-pollution-related health risks in Shanghai be undertaken, especially on the WTP to reduce the risk of premature death from air

pollution. Additional research will be necessary to improve our evaluations of the health outcomes associated with air pollution, which should include: factors that influence WTP (e.g., age, income, education level, pollution level); the relationship between WTP and quality of life; the relationship between private costs and lost output; people’s preferences in trade-offs among risks.

In summary, despite the limitations of our analysis, the results highlight the importance of considering air-pollution-related health effects in decisions about energy options in Shanghai. As we move toward sustainable development and

health, close collaboration between public health policy and energy policy will increase the chances of preventing avoidable health hazards.

The approaches recommended in this analysis can be used to evaluate other regions in China for local and nationwide air-pollution-related health-risk assessments and economic evaluations. But health-impact assessment methods must be improved and refined, especially in dealing with uncertainties, transferring exposure-response functions, and using more common health indicators, such as disability-adjusted life years (DALYs). These improvements will require close cooperation among air-pollution modelers, epidemiologists, economists, and policy makers.

This project was cofunded by the U.S. Environmental Protection Agency (EPA) / U.S. National Renewable Energy Laboratory (NREL) Integrated Environmental Strategies Program (IES), China Council for International Cooperation on Environment and Development, China State Environmental Protection Bureau, China Ministry of Health, and Shanghai Environmental Protection Bureau.

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