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Economic impacts from PM2.5 pollution-related health effects in China: A provincial-level analysis Yang Xie, Hancheng Dai, Huijuan DONG, Tatsuya Hanaoka, and Toshihiko Masui Environ. Sci. Technol., Just Accepted Manuscript • DOI: 10.1021/acs.est.5b05576 • Publication Date (Web): 11 Apr 2016 Downloaded from http://pubs.acs.org on April 11, 2016
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Economic impacts from PM2.5 pollution-related health effects in China: A provincial-level analysis
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Yang Xie1,2, Hancheng Dai*,2, Huijuan Dong2, Tatsuya Hanaoka2, Toshihiko Masui2
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1
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Japan; 2 Center for Social and Environmental Systems Research, National Institute for Environmental Studies, 16-2
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Onogawa, Tsukuba-City, Ibaraki, 305-8506, Japan
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*Corresponding author:
[email protected] Department of Social Engineering, Tokyo Institute of Technology, 2-12-1 Ookayama, Meguro-ku, Tokyo 152-8550,
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Abstract
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This study evaluates the PM2.5 pollution-related health impacts on the national and
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provincial economy of China using a Computable General Equilibrium (CGE) model and the
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latest non-linear exposure-response functions. Results show that the health and economic
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impacts may be substantial in provinces with high PM2.5 concentration. In the WoPol scenario
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without PM2.5 pollution control policy, we estimate that China experiences a 2.00% GDP loss
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and 25.2 billion USD in health expenditure from PM2.5 pollution in 2030. By contrast, with
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control policy in the WPol scenario, a control investment of 101.8 billion USD (0.79% of GDP)
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versus a gain of 1.17% of China’s GDP from improving PM2.5 pollution are projected. At the
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provincial level, GDP loss in 2030 in the WoPol scenario is high in Tianjin (3.08%), Shanghai
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(2.98%), Henan (2.32%), Beijing (2.75%), and Hebei (2.60%), and the top five provinces with
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highest additional health expenditure are Henan, Sichuan, Shandong, Hebei and Jiangsu.
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Controlling PM2.5 pollution could bring positive benefits in two-thirds of provinces. Tianjin,
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Shanghai, Beijing, Henan, Jiangsu, and Hebei experience most benefits from PM2.5 pollution
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control due to higher PM2.5 pollution and dense population distribution. Conversely, the control
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investment is higher than GDP gain in some underdeveloped provinces such as Ningxia,
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Guizhou, Shanxi, Gansu and Yunnan.
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Keywords
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Health impact of PM2.5 pollution; economic impact; computable general equilibrium
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(CGE) model; China
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1. Introduction
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Over its three-decade-long trajectory of fast growth and development, China surpassed the [1]
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US to become the world’s largest carbon emitter in 2007
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faced with severe challenges relating to its environment, particularly the notorious haze
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pollution in recent years. The air quality in China is still much lower than the World Health
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Organization (WHO) recommendation [2, 3], which poses significant threats to human health [4].
. At the same time, China has also
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Outdoor suspended particulate matter (PM) is considered to be the most serious pollutant in
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metropolitan areas, in view of its adverse health effects as a cause of cardiovascular disease,
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respiratory irritation, and pulmonary dysfunction
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2010 were stroke, ischaemic heart disease, and chronic obstructive pulmonary disease, and
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ambient particulate matter pollution is the fourth leading risk factor in China [6]. PM2.5 penetrates
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more deeply into the lung and may reach the alveolar region and poses greater health risks than
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PM10, because its higher surface area per unit mass increases the potential for the adsorption and
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condensation of toxic air pollutants such as oxidant gases, organic compounds, and transition
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metals [7]. Many studies have shown associations between exposure to high PM2.5 concentration
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and higher rates of outpatient visits and hospital admissions for respiratory diseases,
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cardiovascular diseases and cerebrovascular disease [8-15]. Acute exposure to severe air pollutant
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or long-term exposure to air pollutant can increase mortality [16-18].
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[5]
. The leading causes of death in China in
These health problems can pose heavy economic burdens by further increasing health [19] [20]
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expenditure, increasing work day loss, and decreasing the labor supply
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reduced pollution boosts labor productivity and magnifies benefit. In the USA, health-related
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loss of productive time costs employers $225.5 billion per year
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showed that a 19.7% decline in air pollution led to a 1.3 hour (or 3.5%) increase in work hours
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per week [22]. These findings underscore the need to evaluate not only the health impact from air
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pollution, but also the economic impact.
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indicated that
[21]
. A study in Mexico City
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A set of air quality improvement policy proposed in 2005 would bring a welfare gain of
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37 to 49 billion Euros in 2020 for the whole of Europe [23]. Several studies (Table A6 in SI) have
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also been done for China to quantify the economic cost of air pollution arising from its negative
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impact on human health. For example,
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China’s GDP would have increased by US$22 billion in 1975 and US$112 billion (about 5% of
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GDP) in 2005. [25, 26] showed an economic loss of 29.21 billion Yuan, equivalent to 1.35% of the
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regional GDP, was incurred from the impact of PM10 in the Pearl River Delta (PRD) in 2006.
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And the economic loss incurred from PM2.5 pollution in Jing-Jin-Ji area of China was estimated
[24]
found that by improving Ozone and PM pollution,
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[27]
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at 172.9 billion Yuan, about 4.68% of the regional GDP.
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US$ 625.40 million from the PM pollution in Shanghai in 2001, or the equivalent of 1.03% of
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the city’s GDP. In Taiyuan, economic costs of PM pollution amounts to 2.4 – 4.9% and 1-2% of
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the city’s GDP in 2000 and 2015, respectively [28].
estimated a total economic cost of
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Most of the current studies cover only PM10 pollution in a single city, national level or
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province with econometric approaches such as the Willingness to Pay (WTP) and Cost of Illness
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(COI) methods. However, few studies have examined the economic impacts of air pollution at
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the provincial level of China using methodology that captures the full range of interaction in the
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economic system. In this regard, our study focuses on sub-national scale and fills this gap with
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coverage of all provinces (except Tibet). This paper aims to widen the scope of the assessment
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by evaluating the market economic impacts caused by PM2.5 pollution in 30 Chinese provinces,
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assessing the mitigation costs and revealing its regional disparity. More specifically, three
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research questions are addressed: what are the health impacts of PM2.5 pollution in China? How
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the PM2.5 pollution-related health impacts affect the macro economy at the provincial level in
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China? What are the costs and benefits of improving PM2.5 pollution?
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Using the PM2.5 concentration data provided by the Greenhouse Gas and Air Pollution
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Interactions and Synergies (GAINS)-China model and the latest non-linear exposure-response
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functions, we incorporated health-related damages into a Computable General Equilibrium
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(CGE) model. We are able to draw a robust picture of how changes in pollution will affect health
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expenditure, labor supply, and the overall economy about the market impact in China’s 30
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provinces.
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2. Methodology
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As shown in Figure 1, this study combines the GAINS-China model, Asia-Pacific Integrated
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Assessment Asia-Pacific Integrated Assessment (AIM)/CGE model, and a health impact module.
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The GAINS-China model provides annual average PM2.5 concentration data for 30 provinces in
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China. A health module is developed to quantify the health impacts of PM2.5 pollution. Health
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impacts due to mortality and morbidity are converted to annual total medical expenditure and per
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capita work loss caused by PM2.5 pollution, which are then used as a change in the household
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expenditure pattern and labor participation rate by the CGE model to determine the
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macroeconomic impacts. A brief introduction to GAINS-China model and AIM/CGE model is
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provided in the supporting information. The following section focuses on introducing the health
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impact module. GAINS-China model PM2.5 concentration Health module
Morbidity
Expenditure
Mortality
Work loss day
Premature death
CGE Model GDP/welfare loss
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Figure 1: Research flow diagram
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2.1 Health impact module All results are region r, year y, scenario s, and uncertainty range g specific. For simplification, they are omitted in the following description.
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2.1.1 Health endpoint
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Exposure to incremental PM2.5 pollutant leads to health problems called health endpoints, which
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are categorized into morbidity and chronic mortality (Table 1). Most studies[16, 17, 29]indicate that the
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Relative Risk (RR) for the endpoint is in a linear relationship with the concentration level, recent
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studies[30, 31] argue that it is in a non-linear relationship, especially at high concentrations. As showed
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in Eq. 1 and 2, in this study, we adopted both linear and non-linear functions. When the
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concentration is lower than the threshold value of 10 ug/m3, RR is 1, which causes no health impacts.
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Linear function assumes that the concentration-response function (CRF) is a constant. For mortality,
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we adopted China-specific linear function from
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the lookup table in[30]. The number of health endpoints is estimated by multiplying RR with
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population and reported cause-specific mortality rate.
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,,,,,, (C) =
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1,
and cause-specific log-linear function based on
,,, ≤ 0
1 + ,, × ,,, − 0 ,
1 + α )1 − * +−,,,, − 0 ./ ,
01,,,,,, =
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[32]
linear function , ,,, > 0
nonlinear function , ,,, > 0
1,, × ,,,,,, () − 1, for 3456 morbidiey ;4?5
1,, × @,"633 =6
