Many researchers have explored air pollution as an environmental disaster in many industrialized economies (the United States Environmental Protection Agency, 1991; World Health Organization, 2014). However, most of them have failed to draw the link between specific types of air pollution and their health impacts. For example, few researchers explain the relationship between air pollution and life expectancy (the United States Environmental Protection Agency, 1991; World Health Organization, 2014). From this background, the work of Chen, Ebenstein, Greenstone, and Li (2013) addresses some of the misconceptions regarding the effects of air pollution on public health. Although the researchers only focus on analyzing the effects of air pollution in China, their study has a global impact because the effects of air pollution are not exclusive to the Asian nation alone (the United States Environmental Protection Agency, 1991; World Health Organization, 2014). Furthermore, air pollution portends multiple health effects on different demographics. The sampled study analyzes these effects and explains how they affect life expectancy and other measures of health. In this regard, it has a global impact.
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Chen et al. (2013) fill an important research gap through their understanding of the relationship between air pollution and human health outcomes. Their focus (on the development policies of China) highlights this fact because China has reported tremendous economic growth, but with little focus on the effects of the growth on the environment or human health. Chen et al. (2013) draw this link and develop a better comprehension of the relationship between economic growth, environmental sustainability, and human health. In an unrelated sphere of analysis, the tendency by Chinese government officials to understate air pollution effects is the main limitation of the study. This discrepancy emerges because the government often understates China’s carbon footprint. Therefore, relying on government reports on air pollution may limit the study’s reliability. Another limitation of the study is the use of the Huai river demarcation to assess the efficacy of other health care policies formulated by the Chinese government. This step could undermine the study’s reliability and validity because the reported health outcomes could be a product of the new health policies and not from the policies formulated to introduce free heating services.
The researchers chose to use data from multiple World Bank reports on air pollution. This data collection method was suitable for the study because it helped the researchers to get information about a wide geographical area. Indeed, the researchers obtained information about multiple cities that they would have otherwise been unable to get if they used a different methodology. Furthermore, the same data collection method helped the researchers to get information that spanned across a wide study period (1981-2000) and more than 90 cities in China (Chen et al., 2013). They also obtained information about the mortality rates in China from credible sources, such as China’s disease surveillance reports. Such information sources contain representative data developed through a national sample of more than 145 sites (Chen et al., 2013). Therefore, their locations are strategically representative of the national mortality rates. The same advantage is transferable to the sampled study. This way, the researchers’ findings capture wealth variations and the geographic dispersion indices in China. In an unrelated methodology metric, the data analysis process selected by Chen et al. (2013) helped to eliminate bias from the study because the researchers adjusted for the polynomial distance between the northern and southern parts of the Huai River. This step eliminated most types of bias, thereby allowing for causal inference between the northern and southern parts of the river. These steps show that the researchers’ study design was elaborate. Therefore, I would not change any aspects of the methodology.
The evidence gathered from the sampled paper shows that air pollution causes a decline in life expectancy. There are conflicting reports regarding the extent of the decline in life expectancy, but the findings show that the data analysis methods chosen by the researchers explain the disparity. This is why the findings presented by Chen et al. (2013) are five times greater than similar findings conducted using alternative data analysis techniques. For example, the conventional least-squares approach showed a smaller decline in life expectancy, compared to the 2SLS approach used by Chen et al. (2013). From this observation, the researchers observed that other lifestyle factors could have affected life expectancy levels. This influence could also affect the validity of their findings. For example, increased disposable income, stemming from low monthly expenses for household heating, could lead to increased life expectancy if people used the extra money to enroll in health care improvement programs.
Developing countries have had trouble determining the balance between economic growth and environmental sustainability. Most of them have taken the wrong path of pursuing economic growth at the expense of environmental sustainability (World Health Organization, 2014). In this regard, there are many cases of environmental degradation in these countries. The article’s findings are useful in informing policy decisions surrounding economic growth and sustainable development. Particularly, its findings would be instrumental in helping countries strike a balance between economic development and environmental sustainability goals.
Chen, Y., Ebenstein, A., Greenstone, M., & Li, H. (2013). Evidence on the impact of sustained exposure to air pollution on life expectancy from China’s Huai River policy. Proceedings of the National Academy of Sciences of the United States of America, 110(32), 12936–12941.
United States Environmental Protection Agency. (1991). Indoor air facts No. 4 (revised): Sick building syndrome. Web.
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World Health Organization. (2014). Air pollution. Web.