This paper used large scale data from 31 provinces, autonomous regions and municipalities in China. Also it used the GWR model and spatial visualization technique. We used the longitudinal and horizontal comparisons to study the relationship between PM2.5 concentration and the lung cancer mortality. Two main findings emerged from this paper.
Firstly, there is a significant positive correlation between PM2.5 concentration and lung cancer mortality. According the results of linear regression in Table 1, at the 5% significance level, PM2.5 concentration value increases by 10μg / m3, the lung cancer mortality increased by 5.2% correspondingly. In addition, the correlation between the concentration of PM2.5 and the mortality of lung cancer showed a certain spatial distribution with the difference of PM2.5 concentration. Namely, the higher the PM2.5 concentration in some regions is, the greater the intensity of the correlation with lung cancer mortality, and vice versa.
Secondly, with the time going by, the intensity of the impact on lung cancer mortality rate will increase. By comparing the estimates of the coefficients of PM2.5 in 2004 and 2008 in Chinese provinces, autonomous regions and municipalities, this paper holds that the estimate of PM2.5 in 2008 is higher than the estimate of the coefficient in 2004 overall. From 2004 to 2008, the effects of PM2.5 on lung cancer mortality rate has shown a certain degree of improvement.
In addition, for the control variables, the impact of regional economic development level in Table 1 is significantly positive at 10% level. It could be explained as the economic development model being pursued at the expense of the environment pollution in China. Figure 1 also illustrated that the areas with high PM2.5 concentrations are distributed in the eastern China, such as Tianjin, Beijing, Shandong, Jiangsu and other places. The higher PM2.5 concentrations also clearly presents a high level of lung cancer mortality, resulting in the positive correlation between the level of economic development and lung cancer mortality rate. Similarly, lnpopu, the population density index is significant at the 1% level, and the area which is the most densely populated in China, is precisely the region with higher concentration of PM2.5. The significant positive correlation between per capita medical expenses and lung cancer mortality at 1% level in Table 1 is also a scenario that is not consistent with normal expectations. This paper suggests that it may be the indicator itself, and due to the fact that per capita medical costs include the average cost of all treatment, the lung cancer only attributes a small part. So this paper holds that this indicator only reflects the current rapid increase in China’s annual medical expenses and lung cancer mortality. While the other variable, lnmedi, the quantity indicator of regional hospitals presents negative correlation with lung cancer mortality. This indicates that, to a certain extent, a better availability of medical services inhibits lung cancer mortality.
It should be noted that there is a significant positive correlation between PM2.5 and lung cancer mortality as illustrated in Table 1. But in the GWR model, some regression coefficients of PM2.5 are negative (Tibet, Hainan, Qinghai and Inner Mongolia in 2004; Tibet and Qinghai in 2008). This paper argues that the main reason for this is the difference between the model itself and the availability of data. Because the data from all provinces, municipalities and autonomous regions in China are integrated by the linear regression model, the GWR model only uses regional data to regress. Also as the sampling points of lung cancer mortality in China are mostly from economically developed areas and less so in remote areas, therefore this paper suggests that the monitoring data in these areas cannot fully reflect the real relationship.
Based on these findings, the contribution of this study could be concluded: First, considering the spatial heterogeneity of the variables used in this paper, this paper extended the traditional linear regression model, and constructed a geographic weighted regression model based on spatial relations, in order to reflect the spatial correlation between PM2.5 and lung cancer mortality. Second, compared previous study using data from one city or one province, data used in this study was collected from 31 provinces ranging from 2004 to 2008 in China. Particularly, the data for dependent variable (lung cancer death) came from the “China Disease Detection System Death Monitoring Network Report Database”, which was disclosed in recent years. Third, the results of this study indicate that there is a significant positive correlation between PM2.5 concentration and lung cancer mortality. Furthermore, with the time going by, the intensity of the impact of PM2.5 on lung cancer mortality rate will increase. These findings obtained from the GWR model provide more evidence for the relationship between PM2.5 and lung cancer mortality.
Overall, this paper argues that there are rather serious environmental problems in China, and they have significant impacts on citizens’ health. The governance of air pollution should be an urgent problem for the Chinese government to solve. It is suggested that, firstly, China needs to change the economic development model at the expense of the environment. It should combine economic development with environmental protection to construct a sustainable economic development model. Secondly, the large-scale regionalization of China’s air pollution determines that China’s air governance should be coordinated. The government should take actions in the long term, but not just limited to a short-term policy in some regions. For example, during the 2008 Beijing Olympic Games, the Beijing government took strictly control measures on motor vehicles, chemical plants and other air pollution sources, but the fact showed that the effect of these measures is short-term and non-sustainable. Finally, some Chinese people, especially Chinese rural residents generally lack an effective service to deal with PM2.5 pollution. So the Chinese government should strengthen the awareness of public health protection.
For other developing countries, such as countries in South America, there are also some implications based on the present study. First, it is a dual task to develop economy and protect environment. Government of these countries should not neglect environmental protection while developing economy. Some sustainable development policies, such as raising the public awareness of environmental, and imposing a tax on polluting enterprises might be considered. Second, the universal standards which help to reduce pollution, especially air pollution, need to be formulated at the country level. Protection policies made by local government might be no-effective for the regional pollution characteristics. Third, an effective environmental pollution monitoring system and data collection system should be established over the country. Thus, government could make more reasonable measures based on information from these systems.
There are also some limitations in this study. For example, the time span of this paper was only in 2008, because the death data such as the respiratory rate of death in China is not open after 2008, so we cannot get mortality data in the longer time span. Additionally, the GWR model can only deal with cross-sectional data, and it cannot effectively deal with the panel data. However, the panel data contains a larger amount of data, so the results will be more robust. These limitations also imply the future direction for follow-up studies to measure the relationship between PM2.5 and lung cancer mortality more scientifically.