| Air pollution kills an estimated 0.8 million people each year and reduces the world's total life expectancy by 4,600,000 years, according to the World Health Organization (WHO,2002). The economic losses incurred by air pollution each year amounts is huge in China. Therefore, air pollution has substantially impacted on people's health and economic development, calling for an urgent treatment.Due to some problems long existing in China's economic development and energy structure, the air quality continues to deteriorate. Persistent high pollution levels has been observed in certain parts of the country. This phenomenon could be featured with spatial aggregation, namely, air pollution stably persists in large area and high concentrations. In such case, conventional analytic methods work poorly in quantifying the impact of different factors upon air pollution, and a new approach that could reflect the feature is required. Spatial exploratory analysis and spatial econometrics underpinned by spatial economics exactly meet our demand. By adopting spatial exploratory instrument like Geographic Information System (GIS), spatial characteristics are incorporated into our samples, assuring that the influence coming from spatial state are fully considered in corresponding analysis, and thus our subject is accurately and thoroughly described.Spatial exploratory data analysis would be first conducted in this paper to study the current status of air pollution in China. Focusing on several major pollutants, such as sulphur dioxide, nitrogen oxide and particulate matter, China takes a lead in overall emissions as well as per capita emissions. Urban air pollution is severe in particular. Before 2012 when China revised its air quality standards, PMio, SO2 and NO2 were important indicators under monitoring. Comparing dataset covering 31 major Chinese cities for a decade (2003-2012) with outdoor air quality standards provided by WHO, very few samples could reach that standard. All 31 cities failed in the case of PM10, while 28 failed in SO2. Even in the best case of NO2, only 10 cities are up to the standard.After the 2012's revision, pollutants including PM2.5, carbonic monoxide, ozone and so forth were newly included in the air quality monitor network. This paper provided an annual report released in 2013 that contained air quality conditions in 51 Chinese cities, from which we found all samples hit "unhealthy" levels in the case of PM2.5 and ozone, again indicating how severe our air pollution state is. In addition to the general data description, this paper employed GIS to do spatial interpolation analysis on some major pollutants, thus reflecting the air pollution status in China in a holistic manner. The result showed the presence of strong spatial aggregation. To statistically verify this aggregation, Moran index was used to test spatial auto-correlation, and it turned out that all pollutants involved demonstrated significant correlation. Local test was further run to help distinguish every pollutant's spatial aggregation, thus examining the potential transform between distinct spatial aggregations.Following exploratory analysis, we come to spatial quantitative study on selected air pollutants and social-economic development factors, which is of particular interest of this paper. The empirical part covered two types of models, one was estimated with cross sectional data and the other was with panel data. The cross-section equation used annual average emission of PM25, carbonic monoxide, ground-level ozone and PM10 from 51 key environment protection cities as dependent variables, while the panel specification took annual average concentration of PM2.5, sulphur dioxide, nitrogen oxide and particulate matter from 29 provincial administrative units. The results suggested that the selected social-economic development factors would exert large diversified impact on air pollution, and generated spatial spillover effects when estimated with different pollutants. This showed that econometric models considering spatial interaction is a good fit to explain air pollution. This paper also studied model selection with different dataset. As for emission dataset, Spatial Dubin Model (SDM) is more applicable, while for concentration dataset, Spatial Dubin Error Model (SDEM) is much preferred. The rationale behind the choice is that these two models represent two kinds of spatial interaction effects.The results of spatial exploratory analysis and spatial econometric estimation reveal that spatial effects play an important role in explaining the spatial aggregation of air pollution in China, and spatial spillover effects of exogenous variables that are often ignored also generate significant impact. All these findings support that methods that incorporate spatial interaction exhibited considerable explanatory power for studying air pollution. And when it comes to the treatment of the pollution, spatial interaction should also be taken into account to fundamentally address the problem. This paper inevitably has some limitations. For example in the local test, dummy variables could be introduced to the analysis, which could, however, lead to singular matrix with no solution. Future improvements are expected to be realized in the optimization of the spatial econometric algorithm, which thus to be better applied in various field research. |
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