LOCATIONAL DISTRIBUTION AND SPATIAL DIFFUSION OF FOREIGN DIRECT INVESTMENTS FROM HONGKONG AND MACAO IN MAINLAND OF CHINA

Foreign direct investments (or FDIs) have been employed since the early 1980s and they have become more and more immportant in Chinese economic development. However, the roles of FDIs are very different between regions, partly due to the different locational preference of various source countries. Some facts show that FDIs from Hongkong - Macao indicate a strong locational preference. Therefore, this paper attempts to make an empirical research on the locational preference of Hongkong - Macao’s FDIs and their spatial diffusion under the support of statistical data with regrereion analysis. In this paper, three statistical models, including the special location model, the general location model and the spatial diffusion model, are created. The results show that this kind of analysis is successful. The major conclusions are as follows. (1) The optimum location for FDIs from Hongkong - Macao lies in the coastal area, especially Guangdong, Hainan, Jiangsu, Shandong, Fujian provinces. Besides, Hubei Province is also an important radon. (2) The FDIs from HongkongMacao in China have diffused gradually from the coastal provinces to the inland regions, the northem and the metropolis and from the locations that had attracted a large number of investments to their vicinities since the 1990s. (3) The special location factors, such as the herder effect, the unique social and kinship ties are the key factors determining the special locational distribution. (4) The general location and spatial diffusion of Hongkong - Macao’s FDIs are the results of interplay of several economic factors. They are the economic scale and advantage, the growth rate, the laier force and economic extrovert etc.

Size distribution,directional source contributions and pollution status of PM from Chengdu,China during a long-term sampling campaign

Long-term and synchronous monitoring of PMIo and PM2.s was conducted in Chengdu in China from 2007 to 2013. The levels, variations, compositions and size distributions were investigated. The sources were quantified by two-way and three-way receptor models （PMF2, ME2-2way and ME2-3way）, Consistent results were found： the primary source categories contributed 63.4% （PMF2）, 64.8% （ME2-2way） and 66.8% （ME2-Bway） to PMIo, and contributed 60.9% （PMF2）, 65.5% （ME2-2way） and 61.0% （ME2-3way） to PM2.s. Secondary sources contributed 31.8% （PMF2）, 32.9% （ME2-2way） and 31.7% （ME2-3way） to PMIo, and 35.0% （PMF2）, 33.8% （ME2-2way） and 36.0% （ME2-3way） to PM2.s. The size distribution of source categories was estimated better by the ME2-3way method. The three-way model can simultaneously consider chemical species, temporal variability and PM sizes, while a two-way model independently computes datasets of different sizes. A method called source directional apportionment （SDA） was employed to quantify the contributions from various directions for each source category. Crustal dust from east-north-east （ENE） contributed the highest to both PM^o （12.7%） and PMzs （9.7%） in Chengdu, followed by the crustal dust from south-east （SE） for PMao （9.8%） and secondary nitrate ＆ secondary organic carbon from ENE for PMzs （9.6%）. Source contributions from different directions are associated with meteorological conditions, source locations and emission patterns during the sampling period. These findings and methods provide useful tools to better understand PM pollution status and tn dovolon offoctive nolhltion control gtrateMeg.