Valuing the health impacts of air pollution in Hong Kong

Valuing the health impacts of air pollution in Hong Kong

Journal of Asian Economics 17 (2006) 85–102 Valuing the health impacts of air pollution in Hong Kong§ Victor Brajer *, Robert W. Mead, Feng Xiao Depa...

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Journal of Asian Economics 17 (2006) 85–102

Valuing the health impacts of air pollution in Hong Kong§ Victor Brajer *, Robert W. Mead, Feng Xiao Department of Economics, California State University—Fullerton, Fullerton, CA 92834, USA Received 15 March 2005; received in revised form 3 November 2005; accepted 1 December 2005

Abstract Although Hong Kong has relatively clean air compared to other Asian cities, local air quality still falls short of local and international air quality standards for suspended particulates and nitrogen dioxide. Noting the problems associated with trying to transfer and value health effects across countries, this paper uses Hong Kong-based epidemiological studies and Hong Kong-based valuations of health outcomes to estimate and value the impact of successful cleanup activities over a 10-year span. Results indicate potential midrange gains of between US$ 1.6 billion and US$ 5.5 billion. # 2006 Elsevier Inc. All rights reserved. JEL classification: Q51; I12; O13; O53; Q53; R10 Keywords: Urban air pollution; Hong Kong; Health effects; Economic valuation

1. Introduction With an estimated half million urban deaths caused by air pollution every year, Asian cities clearly have an air pollution problem. Compared to many of these cities, Hong Kong has relatively clean air because of a number of pollution control measures enacted over the past 20 years. However, recent evidence suggests that cleanup progress has either slowed or reversed. A 2004 report notes record-breaking pollution levels in 2001, commenting that local residents are becoming concerned as the number of such events increase (Civic Exchange, 2004). More recent Hong Kong news stories report a number of record pollution indexes over the past 3 years,

§ The authors would like to thank two anonymous referees, Orn Bodvarsson and other participants at the Western Economic Association’s 2005 Pacific Rim Conference for their helpful comments on an earlier version of this paper. * Corresponding author. Tel.: +1 714 278 3818; fax: +1 714 278 3097. E-mail address: [email protected] (V. Brajer).

1049-0078/$ – see front matter # 2006 Elsevier Inc. All rights reserved. doi:10.1016/j.asieco.2005.12.002

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including the seventh highest roadside pollution reading ever on New Year’s Eve 2004, and the Hong Kong observatory reports that reduced visibility reached a record high in 2004, partly because of suspended particulates in the atmosphere (Hong Kong Observatory, 2005). Surveys by the Hong Kong Tourism Board find that visitors’ biggest complaint is pollution, and there are concerns that ongoing air pollution problems may deter Hong Kong ventures by foreign enterprises. Another survey of Hong Kong residents indicates that a majority of the population feel as though pollution is worsening, has affected them, and is a reason to move. An examination of specific pollutants reveals that pollution control measures have successfully reduced sulfur dioxide (SO2) below local and World Health Organization (WHO) standards, but levels of total suspended particulates (TSP) and nitrogen dioxide (NO2) continue to be problematic. Ambient levels of these two pollutants periodically exceed local air quality objectives and are consistently higher than the World Health Organization’s 1999 standards. Not only do these pollutants exceed the Hong Kong air quality standards, but the standards themselves have recently received sharp criticism by several sources because they are much less stringent than European standards and constitute unhealthy levels of pollution.1 These criticisms are important because they imply that even if local authorities proclaim Hong Kong’s air to be clean, pollution may still be causing serious adverse effects. Continued pollution levels above Hong Kong and WHO standards create a number of adverse health consequences and a subsequent economic impact. High levels of particulate matter (PM10 or TSP), for example, have been linked to a number of significant health problems, ranging from decreased lung function to increased respiratory and cardiac hospital admissions to premature death. NO2, in recent health studies, has been increasingly associated with a number of respiratory and cardiovascular conditions, including worsening bronchitis, emphysema, heart disease, and even premature cardiovascular mortality. A growing literature exists trying to assess and value the health consequences of these air pollutants in Asia (e.g. Afroz, Hassan, & Ibrahim, 2003; Alberini et al., 1996; Cropper, Simon, Alberini, Arora, & Sharma, 1997; Ostro, 1994; Quah & Boon, 2003), including a newly emergent discussion of the health effects of air pollution in individual Chinese cities (Brajer & Mead, 2003; Kan & Chen, 2004; Kan, Chen, Chen, Fu, and Chen, 2004; Li et al., 2004; Peng et al., 2002) and multiple cities in China (Brajer & Mead, 2004; Mead & Brajer, 2005b). However, none of these studies includes Hong Kong. Those studies that do focus on Hong Kong are either dated by now (Baron et al., 1995) or appear to be intended primarily for local use.2 The purpose of this paper is to assess and value the health benefits of reducing air pollution in Hong Kong by estimating the resulting health improvements of additional cleanup. To do so, we identify several different types of pollution-related health outcomes, estimate the expected reduction in the number of cases from NO2 and PM10 pollution cleanup efforts, and calculate a monetary benefit for each outcome avoided. The rest of the paper proceeds in the following

1 See, for example, the critique by Hopkinson (2004) who goes on to note that the trend in other countries is to revise the standards to make them even stricter as health research warrants. Recent media discussion has focused on a Greenpeace criticism which can be found in a number of news releases and environmental websites including Chong (2005). The Hong Kong Environmental Protection Department’s response can be found at Hong Kong EPD (2005). 2 A number of studies for the Hong Kong Environmental Protection Department can be found at http://www.epd.gov.hk/epd/english/environmentinhk/air/studyrpts/air_studyrpts.html. While publicly available, the reports appear to have been produced as consultancy reports for local policy makers. In this paper, we refer to those reports in order to value some health effects and to obtain some epidemiological (health study) coefficients. However, because these reports have not been subject to a rigorous peer review process, we do not wish to rely exclusively on their results.

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manner. The data and its collection are developed in the next section followed by the creation of various scenarios for reducing each of these two pollutants. Then, the methodology section explains how Hong Kong-based epidemiological studies and valuation studies are used to derive the health consequences and the accompanying dollar values of these cleanup efforts. Results, with accompanying discussion, follow and final comments appear in the last section. 2. Data Hong Kong’s Environmental Protection Department monitors daily air pollution levels at 11 general sites and three roadside stations. Annual and monthly pollution levels for each site are then published in annual air quality reports (Hong Kong EPD, various years). For our initial pollution levels, we use the 2002 reported monthly and annual levels.3 Though Hong Kong is small by ‘‘national’’ standards, pollution levels recorded at the different monitoring stations vary significantly depending upon the surrounding area.4 In addition, population densities also vary across the various districts. The combined variations in pollution and population prompt us to isolate localized population and pollution levels in order to provide a more detailed analysis of the impacts and costs of air pollution. Here, we incorporate the differences in exposure to pollution by identifying the population levels and pollution exposures in the 18 council districts. Base population numbers for each council district come from the 2001 census numbers published by the Hong Kong Government Information Center (Hong Kong GIC, 2001) and are then updated using the assumptions laid out in the next section on Scenario Construction. Using Council District maps and maps of the pollution monitoring stations, the closest monitoring station (or stations) for each Council District are identified. These district level pollution and population values are shown in Table 1.5 In examining the data in Table 1, we note that Hong Kong’s air pollution has a seasonal component. The two pollutants included in this study, PM10 and NO2, are markedly higher in the cool, or winter, season (October through March) than they are in the summer.6 In the cool season, average pollution levels are over 8 mg/m3 above the annual level, reflecting a 16–18 mg/m3 difference between the winter cool season and summer warm season. For particulates, this difference is especially important because it means that winter ambient air quality is worse than both the Hong Kong annual objective and the EU Standard. For NO2, pollution levels are also higher in winter and exceed the WHO standards by substantial levels. After obtaining pollution and population figures, we then acquire baseline mortality and morbidity figures in order to apply the concentration–response health functions (described in the health effect functions, Section 4, below). Using the Department of Health Annual Report 2001/ 3

Our choice to use 2002 pollution baseline levels is a function of data availability. Initially, 2002 was the latest year for which pollution, mortality, morbidity, and weather data were available. 4 Hong Kong monitoring stations are classified broadly as New Town, Roadside, Rural, Urban: residential, and Urban: mixed to reflect the different types of areas being monitored by the stations. Statistical tests for equality of mean monthly levels between all stations strongly reject a hypothesis of equality (NO2: F = 38.48, p = 0.000; PM10: F = 10.07, p = 0.00). 5 In addition, Hong Kong, Chinese, and WHO/EU standards, as well as the average of 47 Chinese cities used by Mead and Brajer (2005), are included for reference purposes. 6 A third pollutant commonly included in other health studies, SO2, is not included here because levels in Hong Kong are low. For 2002, the annual level for SO2 was only 17.4 mg/m3 across all the monitoring stations and the difference between summer and winter levels was only 2 mg/m3. Since these levels are well below the World Health Organization standard of 50 mg/m3, we restrict our subsequent analysis to NO2 and PM10.

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Table 1 2001 Pollution and population levels by Council District Council District

PM10 or RSP (mg/m3)

NO2 (mg/m3)

Annual

Annual

Cool season

Population Cool season

Hong Kong Island Central/Western Wan Chai Eastern Southern

41 63 42 41

53.25 71.81 51.67 53.25

46 75 53 46

57 86.22 62.67 57

261,884 167,146 616,199 290,240

Kowloon Yau Tsim Mong Sham Shui Po Kowloon City Wong Tai Sin Kwun Tong

56.5 53 56.5 53 50.1

66.08 60.33 66.08 60.33 61.6

78 65 78 65 67.4

87.25 73.33 87.25 73.33 79.8

282,020 353,550 381,352 444,630 562,427

New Territories Kwai Tsing Tsuen Wan Tuen Mun Yuen Long North Tai Po Sha Tin Sai Kung Islands

51 53 59 59 59 52 47 47 46

59 59 65.83 65.83 65.83 55.83 53.33 53.33 58.33

64 64 56 56 56 48 45 45 56

69.5 69.5 64.67 64.67 64.67 55.67 52 52 56

477,092 275,527 488,831 449,070 298,657 310,879 628,634 327,689 86,667

District averages/total

51.71

60.04

58.59

67.36

6,702,494

Monitoring stations: monthly levels Average (deviation) Max Min

52.13 (16.49) 61.28 (12.73) 60.44 (24.70) 68.63 (24.09) 92 92 122 122 17 39 7 12

Hong Kong air quality objective (annual) 55 47 Chinese city average (2002) 110.46 China standard 100 40 EU or WHO standarda a

80 37.27 80 40

EU standard is for particulates. WHO standard is for NO2.

2002 (Hong Kong GIC, 2002), which categorizes the cause of all reported deaths, we are able to identify total non-accidental deaths as well as cardiovascular and respiratory specific cases. Similarly, the same report also classifies incidences of hospital patient discharges by category, so we are also able to identify the number of hospital visits by causes. 3. Scenario construction Having compiled the baseline pollution, population, and health data, we next construct several scenarios of future pollution and population levels. Beginning with population, we first use reported population increases for 2002 and 2003 and then project the growth for subsequent years. Based upon mid-year population reports, Hong Kong’s population growth for 2001–2002 was 0.937% and then 0.4% the following year (Hong Kong GIC, 2003a). After the year 2003, we

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use numbers reported by the Hong Kong Task Force on Population Policy, which projects an average annual rate of 1.1% for the 10-year period ending in 2012 (Hong Kong GIC, 2003b). In applying the health effects to the population base, we construct several projected pollution scenarios. To do so, we use an extensive report (Hong Kong EPD, 2002, hereafter called the PRD Report) on regional air pollution in the Pearl River Delta (PRD) region. This report claims that without additional pollution control efforts, regional emissions of a number of different pollutants will increase and air quality in Hong Kong will worsen. In particular, comparing projected levels in 2010 over 1997 levels reveals that the annual average for NO2 will increase 7% and the annual average level of RSP/PM10 pollution will decrease by only 1%. However, should a number of additional recommended pollution controls be implemented, the PRD report projects substantial decreases in overall emissions. Hong Kong’s air quality would thus improve and the new projected 2010 levels (compared to 1997) would be a 12% reduction in NO2 and an 8% reduction in RSP/PM10. Using the report’s analysis, we construct three scenarios. The first scenario, or the ‘‘Business as Usual’’ (BAU) case, constitutes a baseline based upon current pollution levels. For BAU particulates, we project that levels will decline 0.077% annually to reflect the PRD Report’s projection of 1% over the study period. For NO2, we project a BAU annual increase of 0.54% to reflect the projected overall increase of 7%. Two additional scenarios then look at the benefits of more intensive pollution abatement efforts. The first cleanup scenario (hereafter called Scenario 1) again uses the PRD Report and projects a 0.62% annual decline in particulates and a 0.93% annual decline for NO2, reflecting the PRD Report’s forecasted declines of 8% and 12%, respectively. The second scenario (hereafter called the WHO Scenario) assumes that cleanup efforts will decline from existing levels to meet a specified target.7 For NO2, this scenario results in a linear decline to the World Health Organization (WHO) standard of 40 mg/m3 which is intended to identify the threshold level at which adverse health effects begin to appear. For PM10, this scenario projects a linear decline to the EU ambient air quality standard of 40 mg/m3.8 4. Developing health effect functions This section presents the derivation of Hong Kong-specific, pollution-response health functions. The choice to use Hong Kong-based studies is important because even though there exists an extensive health literature for the United States and other developed countries, the appropriateness of relying on extrapolations across countries (see, for example, Krupnick, Harrison, Nickell, & Toman, 1993; Ostro, 1994) has been questioned by many researchers. Alberini and Krupnick (1997), and Murray, McGranahan, and Kuylenstierna (2001), for example, state that the validity of such transfers may be inadequate for a number of reasons (including basic cultural factors, differing perceptions of illness, chemical composition of local 7

Here, we are making the simplifying assumption that in each District, the minimum steps needed to bring that District’s pollution level down to the standards would be taken. In reality, in an aggressive pollution control strategy, steps might be taken to bring the dirtiest areas into compliance with the standards. Such efforts would bring some of the cleaner areas even further below the standards. Our resulting health benefits, particularly for the Scenario 1 case, might then be underestimated. 8 The EU standard is used here, because the WHO (1999) position is that while there is a link between particulates and adverse health outcomes, research to date has not yet established the precise threshold level at which adverse health effects begin to appear. Subsequent WHO documents (2003, 2004) continue to maintain this position.

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pollutants, and the responsiveness of the local population). Cropper et al. (1997) go so far as to state that extrapolations from U.S. studies to developing countries ‘‘are likely to be misleading’’. Hence, for this study we rely completely on health effects studies conducted in Hong Kong.9 We are also careful to use health studies which include more than one pollutant as explanatory variables in developing our simulations. This is important because concentrations of many pollutants (NO2 and particulates, for example) are often highly correlated, making the use of multi-pollutant models essential for disentangling the relative impacts of the pollutants. For example, NO2 coefficient estimates taken from multi-pollutant equations can be as small as onethird (see Stieb, Judek, & Burnett, 2002) the size of their single pollutant equation counterparts. We therefore have only used multi-pollutant equations in generating our benefit estimates. 4.1. Averted mortality—NO2 and PM10 To develop NO2 and PM10 health equations for averted mortality, we turn to a study by Wong, Ma, Hedley, and Lam (2001) that assesses the effects of NO2 and PM10 (along with other pollutants) in both the cool and warm seasons in Hong Kong.10 Using Poisson regressions with daily mortality counts as the dependent variable, the study builds a model that includes a time trend, temperature, humidity, dummy variables for holidays and influenza epidemics, and a variety of air pollution concentrations as independent (explanatory) variables. Results indicate the existence of positive relationships during the cool season between NO2 and cardiovascular mortality, between NO2 and respiratory mortality, and between PM10 and respiratory mortality. Further, from coefficients estimated from these regressions, the authors calculate relative risk (RR) factors for the two pollutants’ health outcomes. For example, for the case of NO2, Wong et al.’s estimated RR factor equals 1.08, implying that the risk of cool season cardiovascular mortality increases by 8% with a 42.1 mg/m3 increase in NO2. These cool season relative risk factors for NO2 and PM10 appear in Table 2. The next step in calculating expected changes in pollution-related deaths is the derivation of a concentration–response function. Here, we adopt the basic natural exponential functional form developed in the U.S. EPA Retrospective Analysis (U.S. EPA, 1997), which evaluates the benefits of emissions controls imposed by the Clean Air Act. Specifically, the following exponential functional form is used in all of our health effect calculations: DC ¼ Cðeb DP  1Þ

(1)

where DC is the predicted additional cases of premature mortality, C the number of baseline cases (here, the cool season baseline death rate), DP the change in ambient pollutant concentration, and b an exponential ‘‘slope’’ factor derived from the health literature. As we have noted, in most of the recent health literature, relative risk factors are reported which relate changes in pollution 9 While an extensive health science literature provides the basis for associating health consequences with various pollutants with research design including animal studies, human clinical studies, and epidemiological studies, only the latter provide data that directly supports the type of economic valuation we are attempting here. Hence, all of the health studies we cite are epidemiological in nature. 10 We acknowledge that the link between NO2 and mortality has not been demonstrated as conclusively as those between SO2 (or PM10) and mortality. However, a number of recent studies do find significant associations for NO2. In addition to the Wong et al. (2001) Hong Kong study that we use, articles by Roemer and Van Wijnen (2001), Wong et al. (2002c), and Zeghnoun et al. (2001) all report statistically significant links between NO2 and certain subcategories of mortality. Our NO2 estimates result from one such subcategory—cool season, cardiovascular deaths.

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Table 2 Deriving concentration–response ‘‘slope’’ factors Pollutant

Relative risk factor

Exponential b

Mortality values NO2-cardiovascular (cool season) NO2-respiratory (cool season) PM10-respiratory (cool season)

1.08 1.08 1.05

0.001828 0.001828 0.0007895

Morbidity values (hospital admissions) Total population (all year) NO2-cardiovascular NO2-respiratory PM10-cardiovascular

0.0009428 0.00129 0.00069756

Age cohort specific (cool season) NO2-cardiovascular (elderly) NO2-respiratory (elderly) PM10-cardiovascular (elderly) NO2-respiratory (children)

0.001222 0.001552 0.0009264 0.001539

levels to the increased odds of developing various health effects. These risk factors are related to the ‘‘b’’ in the EPA concentration–response functions in the following manner: lnðodds ratioÞ : (2) DP Thus, for the case of cool season, NO2-related cardiovascular mortality, the exponential b value is derived as follows: b¼



lnð1:08Þ 42:1

b ¼ 0:001828: The exponential slope factors for cool season NO2- and PM10-related respiratory mortality also appear in Table 2. It is important to note that seven recent NO2 mortality studies from various parts of the world (Bremner et al., 1999; Cadum et al., 1999; Kan & Chen, 2003a,b; Roemer & Van Wijnen, 2001; Wong, Tam, Yu, & Wong, 2002c; Zeghnoun et al., 2001) generate a range of b values from 0.0006 to 0.0022665, with an average value of 0.001756. For the three studies that focused on cardiovascular mortality (Bremmer et al., Cadum et al., and Zeghnoun et al.), the average b-value is 0.001818. Thus, the Wong et al. (2001) results from which we calculate our b value are in line with the evolving health literature for NO2-related mortality effects. 4.2. Averted morbidity (hospital admissions)—NO2 and PM10 To derive Hong Kong-specific health functions for hospital admissions, we turn to two health studies in the peer-reviewed health literature (Wong et al., 2002a and Wong et al., 1999) that examine the statistical associations between respiratory/cardiac admissions and air pollution in Hong Kong. Both Wong et al. (2002a) and Wong et al. (1999) find significant associations between all-year cardiac admissions and NO2, and between all-year respiratory admissions and NO2. Moreover, these associations are detected in multi-pollutant equations,

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adjusted specifically for the presence of particulates. The estimated RR factors from each study allow us to calculate exponential b-values of 0.0016857 and 0.0001998 for cardiac admissions and 0.0016857 and 0.000895974 for the respiratory case. Averaging these gives us an all-year, cardiac admission b-value of 0.0009428 and a respiratory admission b-value of 0.00129. For PM10, the second Wong et al. study reports a significant association between allyear, cardiac hospital admissions and pollution levels. This association exists even after an adjustment is made for the co-pollutant, NO2. This permits us to calculate an exponential bvalue of 0.00069756 for this health outcome. Table 2 presents all of the morbidity b-values. Next, since one important aspect of health benefit studies is that many of the pollutionrelated health effects are suffered by the elderly (adults over 65) and by children, we break down these morbidity benefits into two of their underlying components—benefits accruing to the relatively old and the relatively young. Specifically, we rely on cool-season epidemiological b estimates derived from the Wong et al. (2002a) and Wong et al. (1999) reports, along with two consultancy reports by Wong et al. (1997) and Wong, Ma, Hedley, and Lam, 1998. These reports provide relative risk factors for specific age groups, allowing us to derive age-specific b-values for children less than 15 years of age, and for adults over 65, which also appear in Table 2. The Wong et al. (2002a) study also provides estimates that allow us to generate baseline figures for children’s respiratory and elderly adults’ respiratory and cardiac hospital admissions. By estimating what percentage of total hospital visits are suffered by the elderly and by children in the cool season, we are able to project rough age-specific breakdowns for the all-year numbers described above. Though admittedly a crude measure, this approach does enable us to offer one refinement to our estimate of the mortality effects of Hong Kong’s pollution on the elderly (specifically, the economic valuations of these effects, which we discuss in the valuation section below). 5. Health results Applying the Hong Kong epidemiological functions to our projected levels of pollution in the 18 districts, we are able to quantify the expected number of statistical lives saved and averted hospital admission cases (Tables 3–6). Clearly, the benefits of pollution cleanup may be substantial. In the WHO Scenario projections made for 2003–2012, rising population levels, combined with reductions in pollution, result in a steadily increasing number of averted mortality and morbidity cases. Over the 10-year period, these total nearly 2700 deaths avoided,11 and approximately 45,000 fewer hospital admissions (Tables 3 and 5). Although the Scenario 1 projections (Tables 4 and 6) are only about a third of the WHO Scenario’s, due to the assumed lower level of cleanup success, the overall results are still sizeable. Here, about 825 cases of averted mortality and 16,500 fewer hospital admissions are projected over the decade. In addition to the overall results, the age breakdowns are also quite striking. For the WHO Scenario’s NO2-related respiratory admissions, we calculate that reductions in children’s 11 A reviewer of an earlier draft of this paper correctly pointed out that in the mortality health study which provided our b-value estimates, the co-pollutants being controlled were not the ones of interest. Unfortunately, the existing health literature for Hong Kong did not enable us to find multi-pollutant mortality equations adjusted for the ‘‘correct’’ copollutants. Therefore, we make the conservative assumption that there is a complete (100%) overlap of NO2- and PM10related cases of premature mortality when aggregating our results.

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Table 3 Averted deaths—pollution down to WHO standard District Central/Western Wan Chai Eastern Southern Yau Tsim Mong Sham Shui Po Kowloon City Wong Tai Sin Kwun Tong Kwai Tsing Tsuen Wan Tuen Mun Yuen Long North Tai Po Sha Tin Sai Kung Islands Total

NO2-cardiovascular (cool season)

NO2-respiratory (cool season)

PM10-respiratory (cool season)

Totala (cool season)

42 69 129 47 119 107 162 134 201 128 74 110 101 68 46 74 39 13

26 42 79 28 73 65 99 82 123 78 45 68 62 41 28 45 24 8

7 11 14 8 15 15 20 18 25 18 11 26 24 16 10 17 9 3

68 111 208 75 192 172 261 216 324 206 119 178 163 109 74 119 63 21

1663

1016

267

2679

a

The three columns do not sum to the total due to our assumption of 100% overlap between NO2 and PM10 respiratory deaths.

Table 4 Averted deaths—pollution under Scenario 1 case District Central/Western Wan Chai Eastern Southern Yau Tsim Mong Sham Shui Po Kowloon City Wong Tai Sin Kwun Tong Kwai Tsing Tsuen Wan Tuen Mun Yuen Long North Tai Po Sha Tin Sai Kung Islands Total a

NO2-cardiovascular (cool season)

NO2-respiratory (cool season)

PM10-respiratory (cool season)

Totala (cool season)

17 17 44 19 28 30 38 37 51 38 22 36 33 22 20 37 19 5

10 10 27 12 17 18 23 23 31 23 13 22 20 14 12 23 12 3

2 1 3 2 2 2 3 3 4 3 2 4 3 2 2 4 2 1

27 27 71 31 45 48 61 60 82 61 35 58 53 36 32 60 31 8

513

313

45

826

The three columns do not sum to the total due to our assumption of 100% overlap between NO2 and PM10 respiratory deaths.

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Table 5 Averted hospital admissions—pollution down to WHO standard District Central/Western Wan Chai Eastern Southern Yau Tsim Mong Sham Shui Po Kowloon City Wong Tai Sin Kwun Tong Kwai Tsing Tsuen Wan Tuen Mun Yuen Long North Tai Po Sha Tin Sai Kung Islands Total

NO2-cardiovascular (all year)

NO2-respiratory (all year)

PM10-cardiovascular (all year)

Total (all year)

211 625 929 234 1140 961 1542 1209 1666 1249 721 885 813 541 313 444 231 157

391 1161 1722 433 2121 1785 2868 2245 3095 2319 1339 1640 1507 1002 579 821 428 291

13 264 72 14 317 312 432 392 381 354 243 636 584 388 252 292 152 34

615 2050 2723 681 3578 3058 4842 3846 5142 3922 2303 3161 2904 1931 1144 1557 811 482

13871

25747

5132

44750

PM10-cardiovascular (all year)

Total (all year)

Table 6 Averted hospital admissions—pollution under Scenario 1 case District Central/Western Wan Chai Eastern Southern Yau Tsim Mong Sham Shui Po Kowloon City Wong Tai Sin Kwun Tong Kwai Tsing Tsuen Wan Tuen Mun Yuen Long North Tai Po Sha Tin Sai Kung Islands Total

NO2-cardiovascular (all year)

NO2-respiratory (all year)

165 171 446 182 301 314 407 395 518 417 241 374 344 229 204 386 201 66

305 318 826 338 558 582 754 732 960 773 446 693 636 423 377 715 373 123

40 39 95 44 59 69 79 87 104 90 54 106 98 65 60 108 57 15

510 528 1367 564 918 965 1240 1214 1582 1280 741 1173 1078 717 641 1209 631 204

5361

9932

1269

16562

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admissions and elderly admissions represent 43% and 48% of the totals, respectively. (Note that children and the elderly make up only 14.2% and 12.5% of the overall Hong Kong population.) The Scenario 1 projections generate similar percentages. For the case of cardiac hospital admissions, children’s cases are too few to be estimated, but elderly patients again comprise a disproportionate share of the total. For NO2-driven admissions, we estimate that almost 58% of the total benefits accrue to the elderly. For particulates, this figure approaches 61% of the total morbidity benefit. Clearly, these numbers indicate notable health consequences to Hong Kong’s ongoing air pollution problem. Moreover, a number of these health costs are borne by specific cohorts of the population. Having estimated the levels of health effects, we now turn to assigning economic values. 6. Valuing averted mortality Early attempts to value averted mortality relied on human capital accounting measures. However, these measures provide an incomplete estimate of the loss to the individual and to society of reduced life expectancy, and economists have thus moved toward the more comprehensive willingness-to-pay (WTP) and willingness-to-accept (WTA) measures. One can ascertain WTP or WTA in two basic ways. One is to observe actual market transactions in which individuals experience changes in risk to life and then determine the value to them of avoiding (WTP) or accepting (WTA) that risk. Hedonic, or wage risk, studies are a specific example of this approach. In contrast, the contingent valuation (CV) method relies on surveys to determine personal values for reducing the probability of dying prematurely. For both approaches, the value of reduced annual risk of death, or value of averted death, is a more accurate term for what is being measured, but the expression commonly used is value of a statistical life (VSL). A recent, comprehensive assessment of virtually all available U.S. estimates, from published WTP studies, places most reported values of statistical life in the range of $0.9 million to $20.9 million, with the U.S. EPA currently recommending $6.2 million as their midrange estimate (Dockins et al., 2004). In contrast, fewer studies have taken place in Asia, whose countries have significantly lower incomes, so valuation efforts typically involve transferring economic values from the U.S. to other countries with a simple ‘‘scaling’’ based on national per capita output (or income) ratios between the two countries. Such a procedure contains many drawbacks; the most obvious is the implicit assumption that preferences for health are similar between the country of interest and the U.S. and are determined largely by income (which ignores the potential importance of cultural or other factors in influencing these preferences). This procedure also assumes that the income elasticity of willingness-to-pay (a-WTP) for improved health is equal to 1.0 (or that treating it as 1.0 captures all other factors that may influence the WTP).12 A number of international valuation studies, however, give reason to question the appropriateness of this assumption. In a Bangkok, Thailand study, Chestnut et al. (1997) find that the WTP for avoiding a respiratory illness day actually exceeds what would be predicted following a simple national income adjustment, suggesting that health may be viewed as a basic necessity and ‘‘that those with lower incomes may be willing to pay a higher share of 12 This point is made in Alberini et al. (1996). However, in a theoretical investigation between income and the WTP for environmental goods, Flores and Carson (1997) show that despite being related, knowledge of the ordinary income elasticity of demand is ‘‘insufficient to determine the magnitude or even the sign’’ of the income elasticity of WTP.

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that income to protect their health’’. Alberini and Krupnick (1998, 2000) reach a similar conclusion in a comprehensive health valuation study of three urban areas in Taiwan. Recent valuation work in a number of different countries incorporates the idea of alternative values for a-WTP, though there is no consensus as to what this a-WTP should be. A World Bank study (2002), for example, calculates the health benefits of reducing ozone and particulates for Mexico City, assuming an a-WTP value of 0.40 for their central value calculations. Similarly, Quah and Boon (2003) use an a-WTP of 0.32 in their study of the economic costs of particulate air pollution in Singapore. Viscusi and Aldy (2003a, 2003b), in a meta-valuation review of over 60 mortality studies from 10 countries, determine a range of a-WTP from 0.5 to 0.6. Such values are consistent with the notion that a human life has a certain intrinsic value regardless of a person’s income level. On the other hand, Bowland and Beghin (2001) actually derive a prediction function for developing countries, which accounts for differences in income, and estimate an income elasticity of WTP range of 1.52–2.27 for averted mortality. This, of course, implies that health (or life) is being considered to be a ‘‘luxury’’ good, and that those with higher incomes are willing to pay a disproportionately higher share of that income to reduce the probability of premature death. Finally, in a meta-valuation study, Miller (2000) examines values of statistical life from 68 studies conducted in 13 countries. In the process, he estimates an income elasticity of WTP of 0.96, from which one might conclude that the VSL is basically an income-neutral good. Given such inconclusive results, we choose to bypass the benefit transfer problem by again turning to Hong Kong-specific studies for our economic valuation. First, to generate a low-end estimate of the VSL, we use two Hong Kong-based contingent valuation studies (Wong et al., 2002b; Yee, 1998). In the Yee study, for both respiratory and cardiovascular deaths, Hong Kong residents are asked to value a risk reduction of 0.01%. With a median WTP of HK$ 500, the implied value of a statistical life is HK$ 5 million (in 1997 prices). In the second Hong Kong valuation report, Wong et al. derive a VSL of HK$ 10 million, based on a WHO European study. They then conduct a CV study in Hong Kong to validate this estimate, concluding that the $10 million figure ‘‘is a conservative estimation for the value of a statistical life in Hong Kong’’. Averaging these two figures provides a WTP estimate to avoid premature mortality of HK$ 7.5 million, which we update to 2003 HK dollars and convert to U.S. dollars, using the fixed exchange rate of HK$ 7.8 = US$ 1. The resulting low-range value is $856,614. To generate a mid-range VSL, we turn to a hedonic (wage risk) study by Siebert and Wei (1998), who examine Hong Kong job fatality data and calculate a compensating wage differential for manual workers. They arrive at an estimated VSL of $10.8 million (in 1990 HK dollars). Again, we adjust this number to reflect 2003 prices and convert it into U.S. dollars. Our resulting mid-range figure thus becomes $1,993,836. Finally, to generate a high-range VSL, we borrow from Miller’s (2000) work, which utilizes regressions from the 68 study sample to generate VSLs for additional economies—including Hong Kong. The best estimate for Hong Kong is reported as $3.16 million (in 1995 U.S. dollars). Adjusting this estimate to 2003 prices gives us a value of $3.14 million. To place all of these figures into perspective, we point to several other value-of-life studies conducted in Taiwan and South Korea (Hsueh & Wang, 1987; Kim & Fishback, 1999; Liu & Smith, 1996), newly-industrialized Asian economies with per capita income levels somewhat comparable to Hong Kong’s. These studies yield VSL estimates that we convert into Hong Kongappropriate figures, using a-WTPs of 0.5, 1.0, and 2.0. These conversions give us values ranging from $1.02 million to $2.87 million (in 2003 U.S. dollars). Thus, we feel encouraged that our low, mid and high estimates are good ones.

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7. Valuing averted morbidity Valuation of the two types of hospital admissions (cardiovascular and respiratory) is much more straightforward. Here, we again rely on the 1998 Yee report, which develops estimates of the dollar value of hospital stays. These estimates are based in part on Hong Kong survey work, which indicates that the average WTP to avoid a day at the hospital is HK$ 1736.41 for cardiac admissions and HK$ 1137.45 for respiratory admissions (in 1995 dollars). With the average length of stay equal to 6.33 days for cardiac cases and 4.6 days for respiratory stays, individual valuations of WTP are thus equal to HK$ 10,991.48 and HK$ 5,232.27 for cardiac and respiratory admissions, respectively. The Yee study also notes, however, that some of the health costs of an illness are not always realized by individuals but instead are ‘‘shared through health insurance and public health care subsidies’’. In addition, the study observes that these subsidies are probably particularly relevant for Hong Kong, where hospital room rates are relatively low. We therefore factor in these subsidies, which are estimated to be HK$ 3062 per day, bringing our total economic cost of hospital stays to HK$ 30,373.94 for cardiac admissions and HK$ 19,317.47 for respiratory cases. Converting these figures to 2003 U.S. dollars gives us monetary values of $3,869.67 for cardiac visits and $2,461.07 for respiratory admissions. 8. Health results valuation Applying these valuations to our previously derived health results gives the figures presented in Tables 7 and 8. Under the WHO Standard scenario, the mid-range dollar value of these health improvements is nearly $5.5 billion in 2003 U.S. dollars, with low and high-range estimates of over $2.4 billion and $8.5 billion, respectively. Reflecting the lower numbers of health improvements, the Scenario 1 valuations are also lower than the WHO Scenario’s, but the midrange valuation still approaches $1.7 billion. On an annualized per capita basis, these mid-range valuations amount to $25.32 and $81.74 per year for Scenario 1 and the WHO Standard scenario, respectively. In contrast, total governmental expenditures on health care in 2001/02 were approximately $670 per person (Hong Kong GIC, 2002). Thus, the annual value of the potential health gains amounts to about 3.77% of public health expenditures under Scenario 1. Under the WHO Standard scenario, these potential gains increase to over 12% of public health expenditures. While the overall values are notable and dominated by the mortality valuations, the morbidity valuations are worthy of additional comment. The age-specific breakdowns indicate that these Table 7 Valuation of averted mortality (millions of 2003 U.S. dollars) Scenario

Averted NO2-cardiovascular

Averted NO2-respiratory

Averted PM10-respiratory

Total

Scenario 1 Low value Mid value High value

$439.44 $1022.84 $1610.82

$229.57 $534.35 $841.52

$38.55 $89.72 $141.30

$707.56 $1646.91 $2593.64

WHO standard Low value Mid value High value

$1424.55 $3315.75 $5221.82

$641.60 $1493.38 $2351.86

$228.72 $532.35 $838.38

$2294.87 $5341.48 $8412.06

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Table 8 Valuation of averted morbidity—hospital admissions (millions of 2003 U.S. dollars) Scenario

Averted NO2-cardiovascular

Averted NO2-respiratory

Averted PM10-cardiovascular

Total

Scenario 1 WHO standard

$20.75 $53.68

$24.44 $63.37

$4.91 $19.86

$50.10 $136.90

gains are almost entirely realized by children and the elderly, who together make up slightly over 25% of the total population. Accordingly, concentrating these benefits to this cohort would arguably increase the per person valuation nearly four-fold. Moreover, there is evidence to suggest that the valuation of health costs and benefits should be much higher for children which implies that potential accrued benefits are much larger for members of that particular demographic cohort than the averaged per capita values suggest.13 Finally, some additional discussion of the mortality results, which dominate the overall results, is warranted here. In particular, the issue of the value of a statistical life is an important, yet unresolved, concern in the health valuation literature. While several recent studies do not find a statistically significant relationship between age and VSL (see Bowland & Beghin, 2001; Johannson, 2001; and Zhang, 2002), two recent studies do come up with significant, and specific, estimates of elderly WTP for reducing the risk of premature death. In a paper by Viscusi and Aldy (2003a), workers over 60 have a VSL of $2.5 to $3 million, which is significantly lower than the VSL for prime-age workers. The midpoint of this range, $2.75 million, represents about 45% of the commonly used figure of $6 million, used by many U.S. valuation studies. A second valuation study, by Mount, Weng, Schulze, and Chestnut (2001), considers how families value risk and automobile safety, and finds an elderly VSL estimate of $4.59 million, which is about 72.4% of the working adult value of $6.34 million. In an attempt to address this important issue of elderly VSL, we average the results of these two studies, and come up with an estimate of elderly VSL that is 59% of the value for ‘‘middle age’’ adults. Applying this lower value to our mortality results leads to overall mortality benefit estimates that are about 22% lower than our originally reported figures. 9. Conclusion While there has been some success in air pollution mitigation efforts, there remains an ongoing, long-term air pollution problem in Hong Kong. By projecting two possible cleanup scenarios, this paper finds that there remain significant health gains that could be achieved should Hong Kong further reduce ambient pollution levels. A U.S. dollar valuation of these health results, using mid-range values, leads to projected gains of between $1.7 billion and $5.5 billion over the period 2003–2012. Though we feel that our work does a fair job of capturing the range of potential health benefits, there remain a number of factors which prevent them from being a definitive valuation. First, while we include certain health effects, there are a number of additional pollution-induced health effects, observed in other countries, which we omit because we lack 13 Mead and Brajer (2005a) cite a number of recent valuation studies which argue that adults or parents place a higher value on children’s health costs or health benefits than they do for themselves. This premium may be as much as quadruple the parents’ own valuation.

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